Diagnosis and prognosis of breast cancer patients

ABSTRACT

The present invention relates to genetic markers whose expression is correlated with breast cancer. Specifically, the invention provides sets of markers whose expression patterns can be used to differentiate clinical conditions associated with breast cancer, such as the presence or absence of the estrogen receptor ESR1, and BRCA1 and sporadic tumors, and to provide information on the likelihood of tumor distant metastases within five years of initial diagnosis. The invention relates to methods of using these markers to distinguish these conditions. The invention also provides methods of classifying and treating patients based on prognosis. The invention also relates to kits containing ready-to-use microarrays and computer software for data analysis using the diagnostic, prognostic and statistical methods disclosed herein.

[0001] This application is a continuation-in-part of U.S. application Ser. No. 10/172,118, filed Jun. 14, 2002, which in turn claims benefit of both U.S. Provisional Application No. 60/298,918 filed Jun. 18, 2001 and U.S. Provisional Application No. 60/380,710 filed May 14, 2002, each of which is incorporated by reference herein in its entirety.

[0002] This application includes a Sequence Listing submitted on compact disc, recorded on two compact discs, including one duplicate, containing Filename 9301188999.txt, of size 6,634,550 bytes, created Jan. 13, 2003. The sequence listing on the compact discs is incorporated by reference herein in its entirety.

1. FIELD OF THE INVENTION

[0003] The present invention relates to the identification of marker genes useful in the diagnosis and prognosis of breast cancer. More particularly, the invention relates to the identification of a set of marker genes associated with breast cancer, a set of marker genes differentially expressed in estrogen receptor (+) versus estrogen receptor (−) tumors, a set of marker genes differentially expressed in BRCA1 versus sporadic tumors, and a set of marker genes differentially expressed in sporadic tumors from patients with good clinical prognosis (i.e., metastasis- or disease-free in at least 5 years of follow-up time since diagnosis) versus patients with poor clinical prognosis (i.e., metastasis or disease occurred within 5 years since diagnosis). For each of the marker sets above, the invention further relates to methods of distinguishing the breast cancer-related conditions. The invention further provides methods for determining the course of treatment of a patient with breast cancer.

2. BACKGROUND OF THE INVENTION

[0004] The increased number of cancer cases reported in the United States, and, indeed, around the world, is a major concern. Currently there are only a handful of treatments available for specific types of cancer, and these provide no guarantee of success. In order to be most effective, these treatments require not only an early detection of the malignancy, but a reliable assessment of the severity of the malignancy.

[0005] The incidence of breast cancer, a leading cause of death in women, has been gradually increasing in the United States over the last thirty years. Its cumulative risk is relatively high; 1 in 8 women are expected to develop some type of breast cancer by age 85 in the United States. In fact, breast cancer is the most common cancer in women and the second most common cause of cancer death in the United States. In 1997, it was estimated that 181,000 new cases were reported in the U.S., and that 44,000 people would die of breast cancer (Parker et al., CA Cancer J. Clin. 47:5-27 (1997); Chu et al., J Nat. Cancer Inst. 88:1571-1579 (1996)). While mechanism of tumorigenesis for most breast carcinomas is largely unknown, there are genetic factors that can predispose some women to developing breast cancer (Miki et al., Science, 266:66-71(1994)). The discovery and characterization of BRCA1 and BRCA2 has recently expanded our knowledge of genetic factors which can contribute to familial breast cancer. Germ-line mutations within these two loci are associated with a 50 to 85% lifetime risk of breast and/or ovarian cancer (Casey, Curr. Opin. Oncol. 9:88-93 (1997); Marcus et al., Cancer 77:697-709 (1996)). Only about 5% to 10% of breast cancers are associated with breast cancer susceptibility genes, BRCA1 and BRCA2. The cumulative lifetime risk of breast cancer for women who carry the mutant BRCA1 is predicted to be approximately 92%, while the cumulative lifetime risk for the non-carrier majority is estimated to be approximately 10%. BRCA1 is a tumor suppressor gene that is involved in DNA repair anc cell cycle control, which are both important for the maintenance of genomic stability. More than 90% of all mutations reported so far result in a premature truncation of the protein product with abnormal or abolished function. The histology of breast cancer in BRCA1 mutation carriers differs from that in sporadic cases, but mutation analysis is the only way to find the carrier. Like BRCA1, BRCA2 is involved in the development of breast cancer, and like BRCA1 plays a role in DNA repair. However, unlike BRCA1, it is not involved in ovarian cancer.

[0006] Other genes have been linked to breast cancer, for example c-erb-2 (HER2) and p53 (Beenken et al., Ann. Surg. 233(5):630-638 (2001). Overexpression of c-erb-2 (HER2) and p53 have been correlated with poor prognosis (Rudolph et al., Hum. Pathol. 32(3):311-319 (2001), as has been aberrant expression products of mdm2 (Lukas et al., Cancer Res. 61(7):3212-3219 (2001) and cyclin1 and p27 (Porter & Roberts, International Publication WO98/33450, published Aug. 6, 1998). However, no other clinically useful markers consistently associated with breast cancer have been identified.

[0007] Sporadic tumors, those not currently associated with a known germline mutation, constitute the majority of breast cancers. It is also likely that other, non-genetic factors also have a significant effect on the etiology of the disease. Regardless of the cancer's origin, breast cancer morbidity and mortality increases significantly if it is not detected early in its progression. Thus, considerable effort has focused on the early detection of cellular transformation and tumor formation in breast tissue.

[0008] A marker-based approach to tumor identification and characterization promises improved diagnostic and prognostic reliability. Typically, the diagnosis of breast cancer requires histopathological proof of the presence of the tumor. In addition to diagnosis, histopathological examinations also provide information about prognosis and selection of treatment regimens. Prognosis may also be established based upon clinical parameters such as tumor size, tumor grade, the age of the patient, and lymph node metastasis.

[0009] Diagnosis and/or prognosis may be determined to varying degrees of effectiveness by direct examination of the outside of the breast, or through mammography or other X-ray imaging methods (Jatoi, Am. J. Surg. 177:518-524 (1999)). The latter approach is not without considerable cost, however. Every time a mammogram is taken, the patient incurs a small risk of having a breast tumor induced by the ionizing properties of the radiation used during the test. In addition, the process is expensive and the subjective interpretations of a technician can lead to imprecision. For example, one study showed major clinical disagreements for about one-third of a set of mammograms that were interpreted individually by a surveyed group of radiologists. Moreover, many women find that undergoing a mammogram is a painful experience. Accordingly, the National Cancer Institute has not recommended mammograms for women under fifty years of age, since this group is not as likely to develop breast cancers as are older women. It is compelling to note, however, that while only about 22% of breast cancers occur in women under fifty, data suggests that breast cancer is more aggressive in pre-menopausal women. In clinical practice, accurate diagnosis of various subtypes of breast cancer is important because treatment options, prognosis, and the likelihood of therapeutic response all vary broadly depending on the diagnosis. Accurate prognosis, or determination of distant metastasis-free survival could allow the oncologist to tailor the administration of adjuvant chemotherapy, with women having poorer prognoses being given the most aggressive treatment. Furthermore, accurate prediction of poor prognosis would greatly impact clinical trials for new breast cancer therapies, because potential study patients could then be stratified according to prognosis. Trials could then be limited to patients having poor prognosis, in turn making it easier to discern if an experimental therapy is efficacious.

[0010] To date, no set of satisfactory predictors for prognosis based on the clinical information alone has been identified. The detection of BRCA1 or BRCA2 mutations represents a step towards the design of therapies to better control and prevent the appearance of these tumors. However, there is no equivalent means for the diagnosis of patients with sporadic tumors, the most common type of breast cancer tumor, nor is there a means of differentiating subtypes of breast cancer.

[0011] Adjuvant systemic therapy has been shown to substantially improve the disease-free and overall survival in both premenopausal and postmenopausal women up to age 70 with lymph node negative and lymph node positive breast cancer. See Early Breast Cancer Trialists' Collaborative Group, Lancet 352(9132):930-942 (1998); Early Breast Cancer Trialists' Collaborative Group, Lancet 351(9114):1451-1467 (1998). The absolute benefit from adjuvant treatment is larger for patients with poor prognostic features and this has resulted in the policy to select only these so-called ‘high-risk’ patients for adjuvant chemotherapy. Goldhirsch et al., Meeting highlights: International Consensus Panel on the Treatment of Primary Breast Cancer, Seventh International Conference on Adjuvant Therapy of Primary Breast Cancer, J. Clin. Oncol. 19(18):3817-3827 (2001); Eifel et al., National Institutes of Health Consensus Development Conference Statement: Adjuvant Therapy for Breast Cancer, Nov. 1-3, 2000, J. Natl. Cancer Inst. 93(13):979-989 (2001). Accepted prognostic and predictive factors in breast cancer include age, tumor size, axillary lymph node status, histological tumor type, pathological grade and hormone receptor status. A large number of other factors has been investigated for their potential to predict disease outcome, but these have in general only limited predictive power. Isaacs et al., Semin. Oncol. 28(1):53-67 (2001).

[0012] Using gene expression profiling with cDNA microarrays, Perou et al. showed that there are several subgroups of breast cancer patients based on unsupervised cluster analysis: those of “basal type” and those of “luminal type.” Perou et al., Nature 406(6797):747-752 (2000). These subgroups differ with respect to outcome of disease in patients with locally advanced breast cancer. Sorlie et al., Proc. Natl. Acad. Sci. U.S.A. 98(19):10869-10874 (2001). In addition, microarray analysis has been used to identify diagnostic categories, e.g., BRCA1 and 2 (Hedenfalk et al., N. Engl. J. Med. 344(8):539-548 (2001); van't Veer et al., Nature 415(6871):530-536 (2002)); estrogen receptor (Perou, supra; van't Veer, supra; Gruvberger et al., Cancer. Res. 61(16):5979-5984 (2001)) and lymph node status (West et al., Proc. Natl. Acad. Sci. U.S.A. 98(20):11462-11467 (2001); Ahr et al., Lancet 359(9301):131-132 (2002)).

3. SUMMARY OF THE INVENTION

[0013] The invention provides gene marker sets that distinguish various types and subtypes of breast cancer, and methods of use therefor. In one embodiment, the invention provides a method for classifying a cell sample as ER(+) or ER(−) comprising detecting a difference in the expression of a first plurality of genes relative to a control, said first plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table 1. In specific embodiments, said plurality of genes consists of at least 50, 100, 200, 500, 1000, up to 2,460 of the gene markers listed in Table 1. In another specific embodiment, said plurality of genes consists of each of the genes corresponding to the 2,460 markers listed in Table 2. In another specific embodiment, said plurality consists of the 550 markers listed in Table 2. In another specific embodiment, said control comprises nucleic acids derived from a pool of tumors from individual sporadic patients. In another specific embodiment, said detecting comprises the steps of: (a) generating an ER(+) template by hybridization of nucleic acids derived from a plurality of ER(+) patients within a plurality of sporadic patients against nucleic acids derived from a pool of tumors from individual sporadic patients; (b) generating an ER(−) template by hybridization of nucleic acids derived from a plurality of ER(−) patients within said plurality of sporadic patients against nucleic acids derived from said pool of tumors from individual sporadic patients within said plurality; (c) hybridizing nucleic acids derived from an individual sample against said pool; and (d) determining the similarity of marker gene expression in the individual sample to the ER(+) template and the ER(−) template, wherein if said expression is more similar to the ER(+) template, the sample is classified as ER(+), and if said expression is more similar to the ER(−) template, the sample is classified as ER(−).

[0014] The invention further provides the above methods, applied to the classification of samples as BRCA1 or sporadic, and classifying patients as having good prognosis or poor prognosis. For the BRCA1/sporadic gene markers, the invention provides that the method may be used wherein the plurality of genes is at least 5, 20, 50, 100, 200 or 300 of the BRCA1/sporadic markers listed in Table 3. In a specific embodiment, the optimum 100 markers listed in Table 4 are used. For the prognostic markers, the invention provides that at least 5, 20, 50, 100, or 200 gene markers listed in Table 5 may be used. In a specific embodiment, the optimum 70 markers listed in Table 6 are used.

[0015] The invention further provides that markers may be combined. Thus, in one embodiment, at least 5 markers from Table 1 are used in conjunction with at least 5 markers from Table 3. In another embodiment, at least 5 markers from Table 5 are used in conjunction with at least 5 markers from Table 3. In another embodiment, at least 5 markers from Table 1 are used in conjunction with at least 5 markers from Table 5. In another embodiment, at least 5 markers from each of Tables 1, 3, and 5 are used simultaneously.

[0016] The invention further provides a method for classifying a sample as ER(+) or ER(−) by calculating the similarity between the expression of at least 5 of the markers listed in Table 1 in the sample to the expression of the same markers in an ER(−) nucleic acid pool and an ER(+) nucleic acid pool, comprising the steps of: (a) labeling nucleic acids derived from a sample, with a first fluorophore to obtain a first pool of fluorophore-labeled nucleic acids; (b) labeling with a second fluorophore a first pool of nucleic acids derived from two or more ER(+) samples, and a second pool of nucleic acids derived from two or more ER(−) samples; (c) contacting said first fluorophore-labeled nucleic acid and said first pool of second fluorophore-labeled nucleic acid with said first microarray under conditions such that hybridization can occur, and contacting said first fluorophore-labeled nucleic acid and said second pool of second fluorophore-labeled nucleic acid with said second microarray under conditions such that hybridization can occur, detecting at each of a plurality of discrete loci on the first microarray a first flourescent emission signal from said first fluorophore-labeled nucleic acid and a second fluorescent emission signal from said first pool of second fluorophore-labeled genetic matter that is bound to said first microarray under said conditions, and detecting at each of the marker loci on said second microarray said first fluorescent emission signal from said first fluorophore-labeled nucleic acid and a third fluorescent emission signal from said second pool of second fluorophore-labeled nucleic acid; (d) determining the similarity of the sample to the ER(−) and ER(+) pools by comparing said first fluorescence emission signals and said second fluorescence emission signals, and said first emission signals and said third fluorescence emission signals; and (e) classifying the sample as ER(+) where the first fluorescence emission signals are more similar to said second fluorescence emission signals than to said third fluorescent emission signals, and classifying the sample as ER(−) where the first fluorescence emission signals are more similar to said third fluorescence emission signals than to said second fluorescent emission signals, wherein said similarity is defined by a statistical method. The invention further provides that the other disclosed marker sets may be used in the above method to distinguish BRCA1 from sporadic tumors, and patients with poor prognosis from patients with good prognosis.

[0017] In a specific embodiment, said similarity is calculated by determining a first sum of the differences of expression levels for each marker between said first fluorophore-labeled nucleic acid and said first pool of second fluorophore-labeled nucleic acid, and a second sum of the differences of expression levels for each marker between said first fluorophore-labeled nucleic acid and said second pool of second fluorophore-labeled nucleic acid, wherein if said first sum is greater than said second sum, the sample is classified as ER(−), and if said second sum is greater than said first sum, the sample is classified as ER(+). In another specific embodiment, said similarity is calculated by computing a first classifier parameter P₁ between an ER(+) template and the expression of said markers in said sample, and a second classifier parameter P2 between an ER(−) template and the expression of said markers in said sample, wherein said P1 and P2 are calculated according to the formula:

P _(i)=({right arrow over (z)} _(i) •{right arrow over (y)})/(∥{right arrow over (z)} _(i) ∥·∥{right arrow over (y)}∥),  Equation (1)

[0018] wherein {right arrow over (z)}₁ and {right arrow over (z)}₂ are ER(−) and ER(+) templates, respectively, and are calculated by averaging said second fluorescence emission signal for each of said markers in said first pool of second fluorophore-labeled nucleic acid and said third fluorescence emission signal for each of said markers in said second pool of second fluorophore-labeled nucleic acid, respectively, and wherein {right arrow over (y)} is said first fluorescence emission signal of each of said markers in the sample to be classified as ER(+) or ER(−), wherein the expression of the markers in the sample is similar to ER(+) if P₁<P₂, and similar to ER(−) if P₁>P₂.

[0019] The invention further provides a method for identifying marker genes the expression of which is associated with a particular phenotype. In one embodiment, the invention provides a method for determining a set of marker genes whose expression is associated with a particular phenotype, comprising the steps of: (a) selecting the phenotype having two or more phenotype categories; (b) identifying a plurality of genes wherein the expression of said genes is correlated or anticorrelated with one of the phenotype categories, and wherein the correlation coefficient for each gene is calculated according to the equation

ρ=({right arrow over (c)}•{right arrow over (r)})/(∥{right arrow over (c)}∥·∥{right arrow over (r)}∥)  Equation (2)

[0020] wherein {right arrow over (c)} is a number representing said phenotype category and {right arrow over (r)} is the logarithmic expression ratio across all the samples for each individual gene, wherein if the correlation coefficient has an absolute value of a threshold value or greater, said expression of said gene is associated with the phenotype category, and wherein said plurality of genes is a set of marker genes whose expression is associated with a particular phenotype. The threshold depends upon the number of samples used; the threshold can be calculated as 3×1/{square root}{square root over (n−3)}, where 1/{square root}{square root over (n−3)} is the distribution width and n=the number of samples. In a specific embodiment where n=98, said threshold value is 0.3. In a specific embodiment, said set of marker genes is validated by: (a) using a statistical method to randomize the association between said marker genes and said phenotype category, thereby creating a control correlation coefficient for each marker gene; (b) repeating step (a) one hundred or more times to develop a frequency distribution of said control correlation coefficients for each marker gene; (c) determining the number of marker genes having a control correlation coefficient of a threshold value or above, thereby creating a control marker gene set; and (d) comparing the number of control marker genes so identified to the number of marker genes, wherein if the p value of the difference between the number of marker genes and the number of control genes is less than 0.01, said set of marker genes is validated. In another specific embodiment, said set of marker genes is optimized by the method comprising: (a) rank-ordering the genes by amplitude of correlation or by significance of the correlation coefficients, and (b) selecting an arbitrary number of marker genes from the top of the rank-ordered list. The threshold value depends upon the number of samples tested.

[0021] The invention further provides a method for assigning a person to one of a plurality of categories in a clinical trial, comprising determining for each said person the level of expression of at least five of the prognosis markers listed in Table 6, determining therefrom whether the person has an expression pattern that correlates with a good prognosis or a poor prognosis, and assigning said person to one category in a clinical trial if said person is determined to have a good prognosis, and a different category if that person is determined to have a poor prognosis. The invention further provides a method for assigning a person to one of a plurality of categories in a clinical trial, where each of said categories is associated with a different phenotype, comprising determining for each said person the level of expression of at least five markers from a set of markers, wherein said set of markers includes markers associated with each of said clinical categories, determining therefrom whether the person has an expression pattern that correlates with one of the clinical categories, an assigning said person to one of said categories if said person is determined to have a phenotype associated with that category. The invention further provides a method of classifying a first cell or organism as having one of at least two different phenotypes, said at least two different phenotypes comprising a first phenotype and a second phenotype, said method comprising:

[0022] (a) comparing the level of expression of each of a plurality of genes in a first sample from the first cell or organism to the level of expression of each of said genes, respectively, in a pooled sample from a plurality of cells or organisms, said plurality of cells or organisms comprising different cells or organisms exhibiting said at least two different phenotypes, respectively, to produce a first compared value; (b) comparing said first compared value to a second compared value, wherein said second compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having said first phenotype to the level of expression of each of said genes, respectively, in said pooled sample; (c) comparing said first compared value to a third compared value, wherein said third compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having said second phenotype to the level of expression of each of said genes, respectively, in said pooled sample, (d) optionally carrying out one or more times a step of comparing said first compared value to one or more additional compared values, respectively, each additional compared value being the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having a phenotype different from said first and second phenotypes but included among said at least two different phenotypes, to the level of expression of each of said genes, respectively, in said pooled sample; and (e) determining to which of said second, third and, if present, one or more additional compared values, said first compared value is most similar, wherein said first cell or organism is determined to have the phenotype of the cell or organism used to produce said compared value most similar to said first compared value.

[0023] In a specific embodiment of the above method, said compared values are each ratios of the levels of expression of each of said genes. In another specific embodiment, each of said levels of expression of each of said genes in said pooled sample are normalized prior to any of said comparing steps. In another specific embodiment, normalizing said levels of expression is carried out by dividing each of said levels of expression by the median or mean level of expression of each of said genes or dividing by the mean or median level of expression of one or more housekeeping genes in said pooled sample. In a more specific embodiment, said normalized levels of expression are subjected to a log transform and said comparing steps comprise subtracting said log transform from the log of said levels of expression of each of said genes in said sample from said cell or organism. In another specific embodiment, said at least two different phenotypes are different stages of a disease or disorder. In another specific embodiment, said at least two different phenotypes are different prognoses of a disease or disorder. In yet another specific embodiment, said levels of expression of each of said genes, respectively, in said pooled sample or said levels of expression of each of said genes in a sample from said cell or organism characterized as having said first phenotype, said second phenotype, or said phenotype different from said first and second phenotypes, respectively, are stored on a computer.

[0024] The invention further provides microarrays comprising the disclosed marker sets. In one embodiment, the invention provides a microarray comprising at least 5 markers derived from any one of Tables 1-6, wherein at least 50% of the probes on the microarray are present in any one of Tables 1-6. In more specific embodiments, at least 60%, 70%, 80%, 90%, 95% or 98% of the probes on said microarray are present in any one of Tables 1-6.

[0025] In another embodiment, the invention provides a microarray for distinguishing ER(+) and ER(−) cell samples comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a plurality of genes, said plurality consisting of at least 5 of the genes corresponding to the markers listed in Table 1 or Table 2, wherein at least 50% of the probes on the microarray are present in any one of Table 1 or Table 2. In yet another embodiment, the invention provides a microarray for distinguishing BRCA1-type and sporadic tumor-type cell samples comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a plurality of genes, said plurality consisting of at least 5 of the genes corresponding to the markers listed in Table 3 or Table 4, wherein at least 50% of the probes on the microarray are present in any one of Table 3 or Table 4. In still another embodiment, the invention provides a microarray for distinguishing cell samples from patients having a good prognosis and cell samples from patients having a poor prognosis comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a plurality of genes, said plurality consisting of at least 5 of the genes corresponding to the markers listed in Table 5 or Table 6, wherein at least 50% of the probes on the microarray are present in any one of Table 5 or Table 6. The invention further provides for microarrays comprising at least 5, 20, 50, 100, 200, 500, 100, 1,250, 1,500, 1,750, or 2,000 of the ER-status marker genes listed in Table 1, at least 5, 20, 50, 100, 200, or 300 of the BRCA1 sporadic marker genes listed in Table 3, or at least 5, 20, 50, 100 or 200 of the prognostic marker genes listed in Table 5, in any combination, wherein at least 50%, 60%, 70%, 80%, 90%, 95% or 98% of the probes on said microarrays are present in Table 1, Table 3 and/or Table 5.

[0026] The invention further provides a kit for determining the ER-status of a sample, comprising at least two microarrays each comprising at least 5 of the markers listed in Table 1, and a computer system for determining the similarity of the level of nucleic acid derived from the markers listed in Table 1 in a sample to that in an ER(−) pool and an ER(+) pool, the computer system comprising a processor, and a memory encoding one or more programs coupled to the processor, wherein the one or more programs cause the processor to perform a method comprising computing the aggregate differences in expression of each marker between the sample and ER(−) pool and the aggregate differences in expression of each marker between the sample and ER(+) pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the ER(−) and ER(+) pools, said correlation calculated according to Equation (4). The invention provides for kits able to distinguish BRCA1 and sporadic tumors, and samples from patients with good prognosis from samples from patients with poor prognosis, by inclusion of the appropriate marker gene sets. The invention further provides a kit for determining whether a sample is derived from a patient having a good prognosis or a poor prognosis, comprising at least one microarray comprising probes to at least 5 of the genes corresponding to the markers listed in Table 5, and a computer readable medium having recorded thereon one or more programs for determining the similarity of the level of nucleic acid derived from the markers listed in Table 5 in a sample to that in a pool of samples derived from individuals having a good prognosis and a pool of samples derived from individuals having a good prognosis, wherein the one or more programs cause a computer to perform a method comprising computing the aggregate differences in expression of each marker between the sample and the good prognosis pool and the aggregate differences in expression of each marker between the sample and the poor prognosis pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the good prognosis and poor prognosis pools, said correlation calculated according to Equation (3).

[0027] The invention further provides a method for classifying a breast cancer patient according to prognosis, comprising: (a) comparing the respective levels of expression of at least five genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient to respective control levels of expression of said at least five genes; and (b) classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said levels of expression in said cell sample and said control levels. In a specific embodiment of this method, step (b) comprises determining whether said similarity exceeds one or more predetermined threshold values of similarity. In another more specific embodiment of this method, said control levels are the mean levels of expression of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have no distant metastases within five years of initial diagnosis. In another specific embodiment of this method, said control levels comprise the expression levels of said genes in breast cancer patients who have had no distant metastases within five years of initial diagnosis. In another specific embodiment of this method, said control levels comprise, for each of said at least five genes, mean log intensity values stored on a computer. In another specific embodiment of this method, said control levels comprise, for each of said at least five genes, the mean log intensity values that are listed in Table 7. In another specific embodiment of this method, said comparing step (a) comprises comparing the respective levels of expression of at least ten of said genes for which markers are listed in Table 5 in said cell sample to said respective control levels of said at least ten of said genes, wherein said control levels of expression of said at least ten genes are the average expression levels of each of said at least ten genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis. In another specific embodiment of this method, said comparing step (a) comprises comparing the respective levels of expression of at least 25 of said genes for which markers are listed in Table 5 in said cell sample to said respective control levels of expression of said at least 25 genes, wherein said control levels of expression of said at least 25 genes are the average expression levels of each of said at least 25 genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis. In another specific embodiment of this method, said comparing step (a) comprises comparing the respective levels of expression of each of said genes for which markers are listed in Table 6 in said cell sample to said respective control levels of expression of each of said genes for which markers are listed in Table 6, wherein said control levels of expression of each of said genes for which markers are listed in Table 6 are the average expression levels of each of said genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis.

[0028] The invention further provides for a method for classifying a breast cancer patient according to prognosis, comprising: (a) determining the similarity between the level of expression of each of at least five genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient, to control levels of expression for each respective said at least five genes to obtain a patient similarity value; (b) providing selected first and second threshold values of similarity of said level of expression of each of said at least five genes to said control levels of expression to obtain first and second similarity threshold values, respectively, wherein said second similarity threshold indicates greater similarity to said control than does said first similarity threshold; and (c) classifying said breast cancer patient as having a first prognosis if said patient similarity value exceeds said first and said second similarity threshold values, a second prognosis if said level of expression of said genes exceeds said first similarity threshold value but does not exceed said second similarity threshold value, and a third prognosis if said level of expression of said genes does not exceed said first similarity threshold value or said second similarity threshold value. A specific embodiment of this method comprises determining, prior to step (a), said level of expression of said at least five genes. In another specific embodiment of this method, said determining in step (a) is carried out by a method comprising determining the degree of similarity between the level of expression of each of said at least five genes in a sample taken from said breast cancer patient to the level of expression of each of said at least five genes in a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis. In another specific embodiment of this method, said determining in step (a) is carried out by a method comprising determining the difference between the absolute expression level of each of said at least five genes and the average expression level of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis. In another specific embodiment of this method, said first threshold value and said second threshold value are coefficients of correlation to the mean expression level of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis. In a more specific embodiment of this method, said first threshold similarity value and said second threshold similarity values are selected by a method comprising: (a) rank ordering in descending order said tumor samples that compose said pool of tumor samples by the degree of similarity between the level of expression of each said at least five genes in each of said tumor samples to the mean level of expression of said at least five genes of the remaining tumor samples that compose said pool to obtain a rank-ordered list, said degree of similarity being expressed as a similarity value; (b) determining an acceptable number of false negatives in said classifying step, wherein a false negative is a breast cancer patient for whom the expression levels of said at least five genes in said cell sample predicts that said breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis; (c) determining a similarity value above which in said rank ordered list fewer than said acceptable number of tumor samples are false negatives; (d) selecting said similarity value determined in step (c) as said first threshold similarity value; and (e) selecting a second similarity value, greater than said first similarity value, as said second threshold similarity value. In an even more specific embodiment of this method, said second threshold similarity value is selected in step (e) by a method comprising determining which of said tumor samples, taken from said breast cancer patients having a distant metastasis within the first five years after initial diagnosis, in said rank ordered list has the greatest similarity value, and selecting said greatest similarity value as said second threshold similarity value. In another even more specific embodiment of this method, said first and second threshold similarity values are correlation coefficients, and said first threshold similarity value is 0.4 and said second threshold similarity value is greater than 0.4. In another even more specific embodiment of this method, said first and second threshold similarity values are correlation coefficients, and said second threshold similarity value is 0.636.

[0029] The invention further provides a method of classifying a breast cancer patient according to prognosis comprising the steps of: (a) contacting first nucleic acids derived from a tumor sample taken from said breast cancer patient, and second nucleic acids derived from two or more tumor samples from breast cancer patients who have had no distant metastases within five years of initial diagnosis, with an array under conditions such that hybridization can occur, said array comprising a positionally-addressable ordered array of polynucleotide probes bound to a solid support, said polynucleotide probes being complementary and hybridizable to at least five of the genes respectively for which markers are listed in Table 5, or the RNA encoded by said genes, and wherein at least 50% of the probes on said array are hybridizable to genes respectively for which markers are listed in Table 5, or to the RNA encoded by said genes; (b) detecting at each of a plurality of discrete loci on said array a first fluorescent emission signal from said first nucleic acids and a second fluorescent emission signal from said second nucleic acids that are bound to said array under said conditions; (c) calculating the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes respectively for which markers are listed in Table 5; and (d)classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes respectively for which markers are listed in Table 5.

[0030] The invention further provides for methods of assigning therapeutic regimen to breast cancer patients. In one embodiment, the invention provides a method of assigning a therapeutic regimen to a breast cancer patient, comprising: (a) classifying said patient as having a “poor prognosis,” “intermediate prognosis,” or “very good prognosis” on the basis of the levels of expression of at least five genes for which markers are listed in Table 5; and (b) assigning said patient a therapeutic regimen, said therapeutic regimen (i) comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or (ii) comprising chemotherapy if said patient has any other combination of lymph node status and expression profile.

[0031] The invention also provides a method of assigning a therapeutic regimen to a breast cancer patient, comprising: (a) determining the lymph node status for said patient; (b) determining the level of expression of at least five genes for which markers are listed in Table 5 in a cell sample from said patient, thereby generating an expression profile; (c) classifying said patient as having a “poor prognosis,” “intermediate prognosis,” or “very good prognosis” on the basis of said expression profile; and (d) assigning said patient a therapeutic regimen, said therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and classification. In a specific embodiment of this method, said therapeutic regimen assigned to lymph node negative patients classified as having an “intermediate prognosis” additionally comprises adjuvant hormonal therapy. In another specific embodiment of this method, said classifying step (c) is carried out by a method comprising: (a) rank ordering in descending order a plurality of breast cancer tumor samples that compose a pool of breast cancer tumor samples by the degree of similarity between the level of expression of said at least five genes in each of said tumor samples and the level of expression of said at least five genes across all remaining tumor samples that compose said pool, said degree of similarity being expressed as a similarity value; (b) determining an acceptable number of false negatives in said classifying step, wherein a false negative is a breast cancer patient for whom the expression levels of said at least five genes in said cell sample predicts that said breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis; (c) determining a similarity value above which in said rank ordered list said acceptable number of tumor samples or fewer are false negatives; (d) selecting said similarity value determined in step (c) as a first threshold similarity value; (e) selecting a second similarity value, greater than said first similarity value, as a second threshold similarity value; and (f) determining the similarity between the level of expression of each of said at least five genes in a breast cancer tumor sample from the breast cancer patient and the level of expression of each of said respective at least five genes in said pool, to obtain a patient similarity value, wherein if said patient similarity value equals or exceeds said second threshold similarity value, said patient is classified as having a “very good prognosis”; if said patient similarity value equals or exceeds said first threshold similarity value, but is less than said second threshold similarity value, said patient is classified as having an “intermediate prognosis”; and if said patient similarity value is less than said first threshold similarity value, said patient is classified as having a “poor prognosis.” Another specific embodiment of this method comprises determining the estrogen receptor (ER) status of said patient, wherein if said patient is ER positive and lymph node negative, said therapeutic regimen assigned to said patient additionally comprises adjuvant hormonal therapy. In another specific embodiment of this method, said patient is 52 years of age or younger. In another specific embodiment of this method, said patient has stage I or stage II breast cancer. In yet another specific embodiment of this method, said patient is premenopausal.

[0032] The above methods may be computer-implemented. Thus, in another embodiment, the invention provides a computer program product for classifying a breast cancer patient according to prognosis, the computer program product for use in conjunction with a computer having a memory and a processor, the computer program product comprising a computer readable storage medium having a computer program encoded thereon, wherein said computer program product can be loaded into the one or more memory units of a computer and causes the one or more processor units of the computer to execute the steps of: (a) receiving a first data structure comprising the respective levels of expression of each of at least five genes for which markers are listed in Table 5 in a cell sample taken from said patient; (b) determining the similarity of the level of expression of each of said at least five genes to respective control levels of expression of said at least five genes to obtain a patient similarity value; (c) comparing said patient similarity value to selected first and second threshold values of similarity of said respective levels of expression of each of said at least five genes to said respective control levels of expression of said at least five genes, wherein said second threshold value of similarity indicates greater similarity to said respective control levels of expression of said at least five genes than does said first threshold value of similarity; and (d) classifying said patient as having a first prognosis if said patient similarity value exceeds said first and said second threshold similarity values; a second prognosis if said patient similarity value exceeds said first threshold similarity value but does not exceed said second threshold similarity value; and a third prognosis if said patient similarity value does not exceed said first threshold similarity value or said second threshold similarity value. In a specific embodiment of the computer program product, said first threshold value of similarity and said second threshold value of similarity are values stored in said computer. In another specific embodiment of the computer program product, said respective control levels of expression of said at least five genes is stored in said computer. In another specific embodiment of the computer program product, said first prognosis is a “very good prognosis”; said second prognosis is an “intermediate prognosis”; and said third prognosis is a “poor prognosis”; wherein said computer program may be loaded into the memory and further cause said one or more processor units of said computer to execute the step of assigning said breast cancer patient a therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and expression profile. In a more specific embodiment, said clinical data includes the lymph node and estrogen receptor (ER) status of said breast cancer patient. In yet another specific embodiment, said computer program may be loaded into the memory and further causes said one or more processor units of the computer to execute the steps of receiving a data structure comprising clinical data specific to said breast cancer patient. In another specific embodiment, said respective control levels of expression of said at least five genes comprises a set of single-channel mean hybridization intensity values for each of said at least five genes, stored on said computer readable storage medium. In a more specific embodiment of this computer program product, said single-channel mean hybridization intensity values are log transformed. In another specific embodiment of the computer program product, said computer program product causes said processing unit to perform said comparing step (c) by calculating the difference between the level of expression of each of said at least five genes in said cell sample taken from said breast cancer patient and said respective control levels of expression of said at least five genes. In another specific embodiment of the computer program product, said computer program product causes said processing unit to perform said comparing step (c) by calculating the mean log level of expression of each of said at least five genes in said control to obtain a control mean log expression level for each gene, calculating the log expression level for each of said at least five genes in a breast cancer sample from said patient to obtain a patient log expression level, and calculating the difference between the patient log expression level and the control mean log expression for each of said at least five genes. In another specific embodiment of the computer program product, said computer program product causes said processing unit to perform said comparing step (c) by calculating similarity between the level of expression of each of said at least five genes in said cell sample taken from said patient and said respective control levels of expression of said at least five genes, wherein said similarity is expressed as a similarity value. In a more specific embodiment of this computer program product, said similarity value is a correlation coefficient.

4. BRIEF DESCRIPTION OF THE FIGURES

[0033]FIG. 1 is a Venn-type diagram showing the overlap between the marker sets disclosed herein, including the 2,460 ER markers, the 430 BRCA1/sporadic markers, and the 231 prognosis reporters.

[0034]FIG. 2 shows the experimental procedures for measuring differential changes in mRNA transcript abundance in breast cancer tumors used in this study. In each experiment, Cy5-labeled cRNA from one tumor X is hybridized on a 25 k human microarray together with a Cy3-labeled cRNA pool made of cRNA samples from tumors 1, 2, . . . N. The digital expression data were obtained by scanning and image processing. The error modeling allowed us to assign a p-value to each transcript ratio measurement.

[0035]FIG. 3 Two-dimensional clustering reveals two distinctive types of tumors. The clustering was based on the gene expression data of 98 breast cancer tumors over 4986 significant genes. Dark gray (red) presents up-regulation, light gray (green) represents down-regulation, black indicates no change in expression, and gray indicates that data is not available. 4986 genes were selected that showed a more than two fold change in expression ratios in more than five experiments. Selected clinical data for test results of BR CAI mutations, estrogen receptor (ER), and proestrogen receptor (PR), tumor grade, lymphocytic infiltrate, and angioinvasion are shown at right. Black denotes negative and white denotes positive. The dominant pattern in the lower part consists of 36 patients, out of which 34 are ER-negative (total 39), and 16 are BR CA1-mutation carriers (total 18).

[0036]FIG. 4A portion of unsupervised clustered results as shown in FIG. 3. ESR1 (the estrogen receptor gene) is coregulated with a set of genes that are strongly co-regulated to form a dominant pattern.

[0037]FIG. 5A Histogram of correlation coefficients of significant genes between their expression ratios and estrogen-receptor (ER) status (i.e., ER level). The histogram for experimental data is shown as a gray line. The results of one Monte-Carlo trial is shown in solid black. There are 2,460 genes whose expression data correlate with ER status at a level higher than 0.3 or anti-correlated with ER status at a level lower than −0.3.

[0038]FIG. 5B The distribution of the number of genes that satisfied the same selection criteria (amplitude of correlation above 0.3) from 10,000 Monte-Carlo runs. It is estimated that this set of 2,460 genes reports ER status at a confidence level of p>99.99%.

[0039]FIG. 6 Classification Type 1 and Type 2 error rates as a function of the number (out of 2,460) marker genes used in the classifier. The combined error rate is lowest when approximately 550 marker genes are used.

[0040]FIG. 7 Classification of 98 tumor samples as ER(+) or ER(−) based on expression levels of the 550 optimal marker genes. ER(+) samples (above white line) exhibit a clearly different expression pattern that ER(−) samples (below white line).

[0041]FIG. 8 Correlation between expression levels in samples from each patient and the average profile of the ER(−) group vs. correlation with the ER(+) group. Squares represent samples from clinically ER(−) patients; dots represent samples from clinically ER(+) patients.

[0042]FIG. 9A Histogram of correlation coefficients of gene expression ratio of each significant gene with the BRCA1 mutation status is shown as a solid line. The dashed line indicates a frequency distribution obtained from one Monte-Carlo run. 430 genes exhibited an amplitude of correlation or anti-correlation greater than 0.35.

[0043]FIG. 9B Frequency distribution of the number of genes that exhibit an amplitude of correlation or anti-correlation greater than 0.35 for the 10,000 Monte-Carlo run control. Mean=115 p(n>430)=0.48% and p(>430/2)=9.0%.

[0044]FIG. 10 Classification type 1 and type 2 error rates as a function of the number of discriminating genes used in the classifier (template). The combined error rate is lowest when approximately 100 discriminating marker genes are used.

[0045]FIG. 11A The classification of 38 tumors in the ER(−) group into two subgroups, BR CA1 and sporadic, by using the optimal set of 100 discriminating marker genes. Patients above the white line are characterized by BRCA1-related patterns.

[0046]FIG. 11B Correlation between expression levels in samples from each ER(−) patient and the average profile of the BRCA1 group vs. correlation with the sporadic group.

[0047] Squares represent samples from patients with sporadic-type tumors; dots represent samples from patients carrying the BRCA1 mutation.

[0048]FIG. 12A Histogram of correlation coefficients of gene expression ratio of each significant gene with the prognostic category (distant metastases group and no distant metastases group) is shown as a solid line. The distribution obtained from one Monte-Carlo run is shown as a dashed line. The amplitude of correlation or anti-correlation of 231 marker genes is greater than 0.3.

[0049]FIG. 12B Frequency distribution of the number of genes whose amplitude of correlation or anti-correlation was greater than 0.3 for 10,000 Monte-Carlo runs.

[0050]FIG. 13 The distant metastases group classification error rate for type 1 and type 2 as a function of the number of discriminating genes used in the classifier. The combined error rate is lowest when approximately 70 discriminating marker genes are used.

[0051]FIG. 14 Classification of 78 sporadic tumors into two prognostic groups, distant metastases (poor prognosis) and no distant metastases (good prognosis) using the optimal set of 70 discriminating marker genes. Patients above the white line are characterized by good prognosis. Patients below the white line are characterized by poor prognosis.

[0052]FIG. 15 Correlation between expression levels in samples from each patient and the average profile of the good prognosis group vs. correlation with the poor prognosis group. Squares represent samples from patients having a poor prognosis; dots represent samples from patients having a good prognosis. Red squares represent the ‘reoccurred’ patients and the blue dots represent the ‘non-reoccurred’. A total of 13 out of 78 were mis-classified.

[0053]FIG. 16 The reoccurrence probability as a function of time since diagnosis. Group A and group B were predicted by using a leave-one-out method based on the optimal set of 70 discriminating marker genes. The 43 patients in group A consists of 37 patients from the no distant metastases group and 6 patients from the distant metastases group. The 35 patients in group B consists of 28 patients from the distant metastases group and 7 patients from the no distant metastases group.

[0054]FIG. 17 The distant metastases probability as a function of time since diagnosis for ER(+) (yes) or ER(−) (no) individuals.

[0055]FIG. 18 The distant metastases probability as a function of time since diagnosis for progesterone receptor (PR)(+) (yes) or PR(−) (no) individuals.

[0056]FIGS. 19A, B The distant metastases probability as a function of time since diagnosis. Groups were defined by the tumor grades.

[0057]FIG. 20A Classification of 19 independent sporadic tumors into two prognostic groups, distant metastases and no distant metastases, using the 70 optimal marker genes. Patients above the white line have a good prognosis. Patients below the white line have a poor prognosis.

[0058]FIG. 20B Correlation between expression ratios of each patient and the average expression ratio of the good prognosis group is defined by the training set versus the correlation between expression ratios of each patient and the average expression ratio of the poor prognosis training set. Of nine patients in the good prognosis group, three are from the “distant metastases group”; of ten patients in the good prognosis group, one patient is from the “no distant metastases group”. This error rate of 4 out of 19 is consistent with 13 out of 78 for the initial 78 patients.

[0059]FIG. 20C The reoccurrence probability as a function of time since diagnosis for two groups predicted based on expression of the optimal 70 marker genes.

[0060]FIG. 21A Sensitivity vs. 1-specificity for good prognosis classification.

[0061]FIG. 21B Sensitivity vs. 1-specificity for poor prognosis classification.

[0062]FIG. 21C Total error rate as a function of threshold on the modeled likelihood. Six clinical parameters (ER status, PR status, tumor grade, tumor size, patient age, and presence or absence of angioinvasion) were used to perform the clinical modeling.

[0063]FIG. 22 Comparison of the log(ratio) of individual samples using the “material sample pool” vs. mean subtracted log(intensity) using the “mathematical sample pool” for 70 reporter genes in the 78 sporadic tumor samples. The “material sample pool” was constructed from the 78 sporadic tumor samples.

[0064]FIG. 23A Results of the “leave one out” cross validation based on single channel data. Samples are grouped according to each sample's coefficient of correlation to the average “good prognosis” profile and “poor prognosis” profile for the 70 genes examined. The white line separates samples from patients classified as having poor prognoses (below) and good prognoses (above).

[0065]FIG. 23B Scatter plot of coefficients of correlation to the average expression in “good prognosis” samples and “poor prognosis” samples. The false positive rate (i.e., rate of incorrectly classifying a sample as being from a patient having a good prognosis as being one from a patient having a poor prognosis) was 10 out of 44, and the false negative rate is 6 out of 34.

[0066]FIG. 24A Single-channel hybridization data for samples ranked according to the coefficients of correlation with the good prognosis classifier. Samples classified as “good prognosis” lie above the white line, and those classified as “poor prognosis” lie below.

[0067]FIG. 24B Scatterplot of sample correlation coefficients, with three incorrectly classified samples lying to the right of the threshold correlation coefficient value. The threshold correlation value was set at 0.2727 to limit the false negatives to approximately 10% of the samples.

[0068]FIG. 25A Gene expression pattern of the 70 optimal prognosis marker genes (see Example 4) for a consecutive series of 295 breast carcinomas. Each row represents a prognostic profile of the 70 marker genes for one tumor and each column represents the relative expression abundance of one gene. Red indicates high mRNA expression in the tumor relative to the reference mRNA (pooled mRNA from all tumor samples); green indicates low expression relative to the reference mRNA. The horizontal dotted line is the previously determined separation between good and poor prognosis signature subgroups. Tumors are rank-ordered according to their correlation with the average profile in tumors of good prognosis patients (CI); the most highly correlated tumors lie at the top of the plot.

[0069]FIG. 25B Time in years to distant metastases as a first event (red dots) or the time of follow-up for all other patients (blue dots).

[0070]FIG. 25C Selected clinical characteristics: lymph node status (blue=pN+, white=pN0); metastases as first event (blue=yes, white=no); death (blue=yes, white=no).

[0071] FIGS. 26A-26F Kaplan-Meier plots for the cohort of 295 breast cancer patients. FIG. 26A shows the metastasis-free probability of all 295 patients according to “good prognosis” (n=115, upper line) and “poor prognosis” (n=180, lower line) signature. FIG. 26B shows the overall survival of all 295 patients according to “good prognosis” and “poor prognosis” signature. FIG. 26C shows the metastasis-free probability of lymph node negative patients within the 295 tumor cohort. FIG. 26D shows the overall survival of lymph node negative patients. FIG. 26E shows the metastasis-free probability for lymph node positive patients. FIG. 26F shows the overall survival of lymph node positive patients. For each of the plots, the number of patients who are metastasis-free (FIGS. 26A, C, E) or have survived (FIGS. 26B, D, F), and for whom information is available, at each time point (years) are indicated for “good signature” patients (upper line; upper row of numbers) or “poor signature” patients (lower line; lower row of numbers). For each plot, P indicates the P-value of the log-rank test.

[0072] FIGS. 27A-27G Kaplan-Meier plots of the metastasis-free probabilities for 151 lymph node negative breast cancer patients within the 295 tumor cohort. FIG. 27A shows the metastasis-free probabilities of the “good prognosis” and “poor prognosis” groups as identified by molecular profiling using the 70 optimal marker genes (i.e., “good prognosis” and “poor prognosis” signatures; see Example 4). FIG. 27B shows the metastasis-free probabilities of “low-risk” and “high-risk” groups as identified by “St. Gallen” criteria. FIG. 27C shows the metastasis-free probabilities of “low-risk” and “high-risk” signature groups as identified by “NIH consensus” criteria. FIG. 27D shows the “St. Gallen” “high-risk” group (n=129) divided into “good prognosis” and “poor prognosis” signature groups by profiling. FIG. 27E shows the “NIH” “high-risk” group (n=140) divided into “good prognosis” and “poor prognosis” signature groups by profiling. FIG. 27F shows the “St. Gallen” “low-risk” group (n=22) divided into “good prognosis” and “poor prognosis” signature groups by profiling. FIG. 27G shows the “NIH” “low-risk” group (n=11) divided into “good prognosis” and “poor prognosis” signature groups by profiling. Patients at risk at each time point (years; see description of FIG. 26) are indicated in each plot for “good signature” patients (upper line; upper row of numbers) or “poor signature” patients (lower line; lower row of numbers). P indicates the P-value of the log-rank test.

[0073] FIGS. 28A-28F Kaplan Meier plots for 295 breast cancer patients classified into “very good prognosis,” “intermediate prognosis,” and “poor prognosis” groups. FIG. 28A shows the metastasis-free probability of all 295 patients according to “very good”, “intermediate” and “poor prognosis” signature. FIG. 27B shows the overall survival of all 295 patients according to “very good,” “intermediate,” and “poor prognosis” signature. FIG. 27C shows the metastasis-free probability for lymph node negative patients similarly classified. FIG. 27D shows the overall survival for lymph node negative patients so classified. FIG. 27E shows the metastasis-free probability for lymph node positive patients so classified. FIG. 27F shows the overall survival of lymph node positive patients so classified. Patients at risk at each time point (years; see description of FIG. 26) are indicated in each plot for “very good” signature patients (top line; top row of numbers), “intermediate” signature patients (middle line; middle row of numbers) or “poor prognosis” signature patients (bottom line; bottom row of numbers) patients. P indicates the P-value of the log-rank test.

5. DETAILED DESCRIPTION OF THE INVENTION 5.1 Introduction

[0074] The invention relates to sets of genetic markers whose expression patterns correlate with important characteristics of breast cancer tumors. i.e., estrogen receptor (ER) status, BRCA1 status, and the likelihood of relapse (i.e., distant metastasis or poor prognosis). More specifically, the invention provides for sets of genetic markers that can distinguish the following three clinical conditions. First, the invention relates to sets of markers whose expression correlates with the ER status of a patient, and which can be used to distinguish ER(+) from ER(−) patients. ER status is a useful prognostic indicator, and an indicator of the likelihood that a patient will respond to certain therapies, such as tamoxifen. Also, among women who are ER positive the response rate (over 50%) to hormonal therapy is much higher than the response rate (less 10%) in patients whose ER status is negative. In patients with ER positive tumors the possibility of achieving a hormonal response is directly proportional to the level ER (P. Calabresi and P. S. Schein, MEDICAL ONCOLOGY (2ND ED.), McGraw-Hill, Inc., New York (1993)). Second, the invention further relates to sets of markers whose expression correlates with the presence of BRCA1 mutations, and which can be used to distinguish BRCA1-type tumors from sporadic tumors. Third, the invention relates to genetic markers whose expression correlates with clinical prognosis, and which can be used to distinguish patients having good prognoses (i.e., no distant metastases of a tumor within five years) from poor prognoses (i.e., distant metastases of a tumor within five years). Methods are provided for use of these markers to distinguish between these patient groups, and to determine general courses of treatment. Microarrays comprising these markers are also provided, as well as methods of constructing such microarrays. Each markers correspond to a gene in the human genome, i.e., such marker is identifiable as all or a portion of a gene. Finally, because each of the above markers correlates with a certain breast cancer-related conditions, the markers, or the proteins they encode, are likely to be targets for drugs against breast cancer.

5.2 Definitions

[0075] As used herein, “BRCA1 tumor” means a tumor having cells containing a mutation of the BRCA1 locus.

[0076] The “absolute amplitude” of correlation expressions means the distance, either positive or negative, from a zero value; i.e., both correlation coefficients −0.35 and 0.35 have an absolute amplitude of 0.35.

[0077] “Status” means a state of gene expression of a set of genetic markers whose expression is strongly correlated with a particular phenotype. For example, “ER status” means a state of gene expression of a set of genetic markers whose expression is strongly correlated with that of ESR1 (estrogen receptor gene), wherein the pattern of these genes' expression differs detectably between tumors expressing the receptor and tumors not expressing the receptor.

[0078] “Good prognosis” means that a patient is expected to have no distant metastases of a breast tumor within five years of initial diagnosis of breast cancer.

[0079] “Poor prognosis” means that a patient is expected to have distant metastases of a breast tumor within five years of initial diagnosis of breast cancer.

[0080] “Marker” means an entire gene, or an EST derived from that gene, the expression or level of which changes between certain conditions. Where the expression of the gene correlates with a certain condition, the gene is a marker for that condition.

[0081] “Marker-derived polynucleotides” means the RNA transcribed from a marker gene, any cDNA or cRNA produced therefrom, and any nucleic acid derived therefrom, such as synthetic nucleic acid having a sequence derived from the gene corresponding to the marker gene.

[0082] A “similarity value” is a number that represents the degree of similarity between two things being compared. For example, a similarity value may be a number that indicates the overall similarity between a patient's expression profile using specific phenotype-related markers and a control specific to that phenotype (for instance, the similarity to a “good prognosis” template, where the phenotype is a good prognosis). The similarity value may be expressed as a similarity metric, such as a correlation coefficient, or may simply be expressed as the expression level difference, or the aggregate of the expression level differences, between a patient sample and a template.

5.3 Markers Useful in Diagnosis and Prognosis of Breast Cancer 5.3.1 Marker Sets

[0083] The invention provides a set of 4,986 genetic markers whose expression is correlated with the existence of breast cancer by clustering analysis. A subset of these markers identified as useful for diagnosis or prognosis is listed as SEQ ID NOS: 1-2,699. The invention also provides a method of using these markers to distinguish tumor types in diagnosis or prognosis.

[0084] In one embodiment, the invention provides a set of 2,460 genetic markers that can classify breast cancer patients by estrogen receptor (ER) status; i.e., distinguish between ER(+) and ER(−) patients or tumors derived from these patients. ER status is an important indicator of the likelihood of a patient's response to some chemotherapies (i.e., tamoxifen). These markers are listed in Table 1. The invention also provides subsets of at least 5, 10, 25, 50, 100, 200, 300, 400, 500, 750, 1,000, 1,250, 1,500, 1,750 or 2,000 genetic markers, drawn from the set of 2,460 markers, which also distinguish ER(+) and ER(−) patients or tumors. Preferably, the number of markers is 550. The invention further provides a set of 550 of the 2,460 markers that are optimal for distinguishing ER status (Table 2). The invention also provides a method of using these markers to distinguish between ER(+) and ER(−) patients or tumors derived therefrom.

[0085] In another embodiment, the invention provides a set of 430 genetic markers that can classify ER(−) breast cancer patients by BRCA1 status; i.e., distinguish between tumors containing a BRCA1 mutation and sporadic tumors. These markers are listed in Table 3. The invention further provides subsets of at least 5, 10 20, 30, 40, 50, 75, 100, 150, 200, 250, 300 or 350 markers, drawn from the set of 430 markers, which also distinguish between tumors containing a BRCA1 mutation and sporadic tumors. Preferably, the number of markers is 100. A preferred set of 100 markers is provided in Table 4. The invention also provides a method of using these markers to distinguish between BRCA1 and sporadic patients or tumors derived therefrom.

[0086] In another embodiment, the invention provides a set of 231 genetic markers that can distinguish between patients with a good breast cancer prognosis (no breast cancer tumor distant metastases within five years) and patients with a poor breast cancer prognosis (tumor distant metastases within five years). These markers are listed in Table 5. The invention also provides subsets of at least 5, 10, 20, 30, 40, 50, 75, 100, 150 or 200 markers, drawn from the set of 231, which also distinguish between patients with good and poor prognosis. A preferred set of 70 markers is provided in Table 6. In a specific embodiment, the set of markers consists of the twelve kinase-related markers and the seven cell division- or mitosis-related markers listed. The invention also provides a method of using the above markers to distinguish between patients with good or poor prognosis. In another embodiment, the invention provides a method of using the prognosis-associated markers to distinguish between patients having a very good prognosis, an intermediate prognosis, and a poor prognosis, and thereby determining the appropriate combination of adjuvant or hormonal therapy. TABLE 1 2,460 gene markers that distinguish ER(+) and ER(−) cell samples. GenBank Accession Number SEQ ID NO AA555029_RC SEQ ID NO 1 AB000509 SEQ ID NO 2 AB001451 SEQ ID NO 3 AB002301 SEQ ID NO 4 AB002308 SEQ ID NO 5 AB002351 SEQ ID NO 6 AB002448 SEQ ID NO 7 AB006628 SEQ ID NO 9 AB006630 SEQ ID NO 10 AB006746 SEQ ID NO 11 AB007458 SEQ ID NO 12 AB007855 SEQ ID NO 13 AB007857 SEQ ID NO 14 AB007863 SEQ ID NO 15 AB007883 SEQ ID NO 16 AB007896 SEQ ID NO 17 AB007899 SEQ ID NO 18 AB007916 SEQ ID NO 19 AB007950 SEQ ID NO 20 AB011087 SEQ ID NO 21 AB011089 SEQ ID NO 22 AB011104 SEQ ID NO 23 AB011105 SEQ ID NO 24 AB011121 SEQ ID NO 25 AB011132 SEQ ID NO 26 AB011152 SEQ ID NO 27 AB011179 SEQ ID NO 28 AB014534 SEQ ID NO 29 AB014568 SEQ ID NO 30 AB018260 SEQ ID NO 31 AB018268 SEQ ID NO 32 AB018289 SEQ ID NO 33 AB018345 SEQ ID NO 35 AB020677 SEQ ID NO 36 AB020689 SEQ ID NO 37 AB020695 SEQ ID NO 38 AB020710 SEQ ID NO 39 AB023139 SEQ ID NO 40 AB023151 SEQ ID NO 41 AB023152 SEQ ID NO 42 AB023163 SEQ ID NO 43 AB023173 SEQ ID NO 44 AB023211 SEQ ID NO 45 AB024704 SEQ ID NO 46 AB028985 SEQ ID NO 47 AB028986 SEQ ID NO 48 AB028998 SEQ ID NO 49 AB029031 SEQ ID NO 51 AB032951 SEQ ID NO 52 AB032966 SEQ ID NO 53 AB032969 SEQ ID NO 54 AB032977 SEQ ID NO 56 AB033007 SEQ ID NO 58 AB033034 SEQ ID NO 59 AB033035 SEQ ID NO 60 AB033040 SEQ ID NO 61 AB033049 SEQ ID NO 63 AB033050 SEQ ID NO 64 AB033053 SEQ ID NO 65 AB033055 SEQ ID NO 66 AB033058 SEQ ID NO 67 AB033073 SEQ ID NO 68 AB033092 SEQ ID NO 69 AB033111 SEQ ID NO 70 AB036063 SEQ ID NO 71 AB037720 SEQ ID NO 72 AB037743 SEQ ID NO 74 AB037745 SEQ ID NO 75 AB037756 SEQ ID NO 76 AB037765 SEQ ID NO 77 AB037778 SEQ ID NO 78 AB037791 SEQ ID NO 79 AB037793 SEQ ID NO 80 AB037802 SEQ ID NO 81 AB037806 SEQ ID NO 82 AB037809 SEQ ID NO 83 AB037836 SEQ ID NO 84 AB037844 SEQ ID NO 85 AB037845 SEQ ID NO 86 AB037848 SEQ ID NO 87 AB037863 SEQ ID NO 88 AB037864 SEQ ID NO 89 AB040881 SEQ ID NO 90 AB040900 SEQ ID NO 91 AB040914 SEQ ID NO 92 AB040926 SEQ ID NO 93 AB040955 SEQ ID NO 94 AB040961 SEQ ID NO 95 AF000974 SEQ ID NO 97 AF005487 SEQ ID NO 98 AF007153 SEQ ID NO 99 AF007155 SEQ ID NO 100 AF015041 SEQ ID NO 101 AF016004 SEQ ID NO 102 AF016495 SEQ ID NO 103 AF020919 SEQ ID NO 104 AF026941 SEQ ID NO 105 AF035191 SEQ ID NO 106 AF035284 SEQ ID NO 107 AF035318 SEQ ID NO 108 AF038182 SEQ ID NO 109 AF038193 SEQ ID NO 110 AF042838 SEQ ID NO 111 AF044127 SEQ ID NO 112 AF045229 SEQ ID NO 113 AF047002 SEQ ID NO 114 AF047826 SEQ ID NO 115 AF049460 SEQ ID NO 116 AF052101 SEQ ID NO 117 AF052117 SEQ ID NO 118 AF052155 SEQ ID NO 119 AF052159 SEQ ID NO 120 AF052176 SEQ ID NO 122 AF052185 SEQ ID NO 123 AF055270 SEQ ID NO 126 AF058075 SEQ ID NO 127 AF061034 SEQ ID NO 128 AF063725 SEQ ID NO 129 AF063936 SEQ ID NO 130 AF065241 SEQ ID NO 131 AF067972 SEQ ID NO 132 AF070536 SEQ ID NO 133 AF070552 SEQ ID NO 134 AF070617 SEQ ID NO 135 AF073770 SEQ ID NO 138 AF076612 SEQ ID NO 139 AF079529 SEQ ID NO 140 AF090913 SEQ ID NO 142 AF095719 SEQ ID NO 143 AF098641 SEQ ID NO 144 AF099032 SEQ ID NO 145 AF100756 SEQ ID NO 146 AF101051 SEQ ID NO 147 AF103375 SEQ ID NO 148 AF103458 SEQ ID NO 149 AF103530 SEQ ID NO 150 AF103804 SEQ ID NO 151 AF111849 SEQ ID NO 152 AF112213 SEQ ID NO 153 AF113132 SEQ ID NO 154 AF116682 SEQ ID NO 156 AF118224 SEQ ID NO 157 AF118274 SEQ ID NO 158 AF119256 SEQ ID NO 159 AF119665 SEQ ID NO 160 AF121255 SEQ ID NO 161 AF131748 SEQ ID NO 162 AF131753 SEQ ID NO 163 AF131760 SEQ ID NO 164 AF131784 SEQ ID NO 165 AF131828 SEQ ID NO 166 AF135168 SEQ ID NO 167 AF141882 SEQ ID NO 168 AF148505 SEQ ID NO 169 AF149785 SEQ ID NO 170 AF151810 SEQ ID NO 171 AF152502 SEQ ID NO 172 AF155120 SEQ ID NO 174 AF159092 SEQ ID NO 175 AF161407 SEQ ID NO 176 AF161553 SEQ ID NO 177 AF164104 SEQ ID NO 178 AF167706 SEQ ID NO 179 AF175387 SEQ ID NO 180 AF176012 SEQ ID NO 181 AF186780 SEQ ID NO 182 AF217508 SEQ ID NO 184 AF220492 SEQ ID NO 185 AF224266 SEQ ID NO 186 AF230904 SEQ ID NO 187 AF234532 SEQ ID NO 188 AF257175 SEQ ID NO 189 AF257659 SEQ ID NO 190 AF272357 SEQ ID NO 191 AF279865 SEQ ID NO 192 AI497657_RC SEQ ID NO 193 AJ012755 SEQ ID NO 194 AJ223353 SEQ ID NO 195 AJ224741 SEQ ID NO 196 AJ224864 SEQ ID NO 197 AJ225092 SEQ ID NO 198 AJ225093 SEQ ID NO 199 AJ249377 SEQ ID NO 200 AJ270996 SEQ ID NO 202 AJ272057 SEQ ID NO 203 AJ275978 SEQ ID NO 204 AJ276429 SEQ ID NO 205 AK000004 SEQ ID NO 206 AK000005 SEQ ID NO 207 AK000106 SEQ ID NO 208 AK000142 SEQ ID NO 209 AK000168 SEQ ID NO 210 AK000345 SEQ ID NO 212 AK000543 SEQ ID NO 213 AK000552 SEQ ID NO 214 AK000643 SEQ ID NO 216 AK000660 SEQ ID NO 217 AK000689 SEQ ID NO 218 AK000770 SEQ ID NO 220 AK000933 SEQ ID NO 221 AK001100 SEQ ID NO 223 AK001164 SEQ ID NO 224 AK001166 SEQ ID NO 225 AK001295 SEQ ID NO 226 AK001380 SEQ ID NO 227 AK001423 SEQ ID NO 228 AK001438 SEQ ID NO 229 AK001492 SEQ ID NO 230 AK001499 SEQ ID NO 231 AK001630 SEQ ID NO 232 AK001872 SEQ ID NO 234 AK001890 SEQ ID NO 235 AK002016 SEQ ID NO 236 AK002088 SEQ ID NO 237 AK002206 SEQ ID NO 240 AL035297 SEQ ID NO 241 AL049265 SEQ ID NO 242 AL049365 SEQ ID NO 244 AL049370 SEQ ID NO 245 AL049381 SEQ ID NO 246 AL049397 SEQ ID NO 247 AL049415 SEQ ID NO 248 AL049667 SEQ ID NO 249 AL049801 SEQ ID NO 250 AL049932 SEQ ID NO 251 AL049935 SEQ ID NO 252 AL049943 SEQ ID NO 253 AL049949 SEQ ID NO 254 AL049963 SEQ ID NO 255 AL049987 SEQ ID NO 256 AL050021 SEQ ID NO 257 AL050024 SEQ ID NO 258 AL050090 SEQ ID NO 259 AL050148 SEQ ID NO 260 AL050151 SEQ ID NO 261 AL050227 SEQ ID NO 262 AL050367 SEQ ID NO 263 AL050370 SEQ ID NO 264 AL050371 SEQ ID NO 265 AL050372 SEQ ID NO 266 AL050388 SEQ ID NO 267 AL079276 SEQ ID NO 268 AL079298 SEQ ID NO 269 AL080079 SEQ ID NO 271 AL080192 SEQ ID NO 273 AL080199 SEQ ID NO 274 AL080209 SEQ ID NO 275 AL080234 SEQ ID NO 277 AL080235 SEQ ID NO 278 AL096737 SEQ ID NO 279 AL110126 SEQ ID NO 280 AL110139 SEQ ID NO 281 AL110202 SEQ ID NO 283 AL110212 SEQ ID NO 284 AL110260 SEQ ID NO 285 AL117441 SEQ ID NO 286 AL117452 SEQ ID NO 287 AL117477 SEQ ID NO 288 AL117502 SEQ ID NO 289 AL117523 SEQ ID NO 290 AL117595 SEQ ID NO 291 AL117599 SEQ ID NO 292 AL117600 SEQ ID NO 293 AL117609 SEQ ID NO 294 AL117617 SEQ ID NO 295 AL117666 SEQ ID NO 296 AL122055 SEQ ID NO 297 AL133033 SEQ ID NO 298 AL133035 SEQ ID NO 299 AL133074 SEQ ID NO 301 AL133096 SEQ ID NO 302 AL133105 SEQ ID NO 303 AL133108 SEQ ID NO 304 AL133572 SEQ ID NO 305 AL133619 SEQ ID NO 307 AL133622 SEQ ID NO 308 AL133623 SEQ ID NO 309 AL133624 SEQ ID NO 310 AL133632 SEQ ID NO 311 AL133644 SEQ ID NO 312 AL133645 SEQ ID NO 313 AL133651 SEQ ID NO 314 AL137310 SEQ ID NO 316 AL137316 SEQ ID NO 317 AL137332 SEQ ID NO 318 AL137342 SEQ ID NO 319 AL137362 SEQ ID NO 321 AL137381 SEQ ID NO 322 AL137407 SEQ ID NO 323 AL137448 SEQ ID NO 324 AL137502 SEQ ID NO 326 AL137514 SEQ ID NO 327 AL137540 SEQ ID NO 328 AL137566 SEQ ID NO 330 AL137615 SEQ ID NO 331 AL137673 SEQ ID NO 335 AL137718 SEQ ID NO 336 AL137736 SEQ ID NO 337 AL137751 SEQ ID NO 338 AL137761 SEQ ID NO 339 AL157431 SEQ ID NO 340 AL157432 SEQ ID NO 341 AL157454 SEQ ID NO 342 AL157476 SEQ ID NO 343 AL157480 SEQ ID NO 344 AL157482 SEQ ID NO 345 AL157484 SEQ ID NO 346 AL157492 SEQ ID NO 347 AL157505 SEQ ID NO 348 AL157851 SEQ ID NO 349 AL160131 SEQ ID NO 350 AL161960 SEQ ID NO 351 AL162049 SEQ ID NO 352 AL355708 SEQ ID NO 353 D13643 SEQ ID NO 355 D14678 SEQ ID NO 356 D25328 SEQ ID NO 357 D26070 SEQ ID NO 358 D26488 SEQ ID NO 359 D31887 SEQ ID NO 360 D38521 SEQ ID NO 361 D38553 SEQ ID NO 362 D42043 SEQ ID NO 363 D42047 SEQ ID NO 364 D43950 SEQ ID NO 365 D50402 SEQ ID NO 366 D50914 SEQ ID NO 367 D55716 SEQ ID NO 368 D80001 SEQ ID NO 369 D80010 SEQ ID NO 370 D82345 SEQ ID NO 371 D83781 SEQ ID NO 372 D86964 SEQ ID NO 373 D86978 SEQ ID NO 374 D86985 SEQ ID NO 375 D87076 SEQ ID NO 376 D87453 SEQ ID NO 377 D87469 SEQ ID NO 378 D87682 SEQ ID NO 379 G26403 SEQ ID NO 380 J02639 SEQ ID NO 381 J04162 SEQ ID NO 382 K02403 SEQ ID NO 384 L05096 SEQ ID NO 385 L10333 SEQ ID NO 386 L11645 SEQ ID NO 387 L21934 SEQ ID NO 388 L22005 SEQ ID NO 389 L48692 SEQ ID NO 391 M12758 SEQ ID NO 392 M15178 SEQ ID NO 393 M21551 SEQ ID NO 394 M24895 SEQ ID NO 395 M26383 SEQ ID NO 396 M27749 SEQ ID NO 397 M28170 SEQ ID NO 398 M29873 SEQ ID NO 399 M29874 SEQ ID NO 400 M30448 SEQ ID NO 401 M30818 SEQ ID NO 402 M31932 SEQ ID NO 403 M37033 SEQ ID NO 404 M55914 SEQ ID NO 405 M63438 SEQ ID NO 406 M65254 SEQ ID NO 407 M68874 SEQ ID NO 408 M73547 SEQ ID NO 409 M77142 SEQ ID NO 410 M80899 SEQ ID NO 411 M83822 SEQ ID NO 412 M90657 SEQ ID NO 413 M93718 SEQ ID NO 414 M96577 SEQ ID NO 415 NM_000022 SEQ ID NO 417 NM_000044 SEQ ID NO 418 NM_000050 SEQ ID NO 419 NM_000057 SEQ ID NO 420 NM_000060 SEQ ID NO 421 NM_000064 SEQ ID NO 422 NM_000073 SEQ ID NO 424 NM_000077 SEQ ID NO 425 NM_000086 SEQ ID NO 426 NM_000087 SEQ ID NO 427 NM_000095 SEQ ID NO 429 NM_000096 SEQ ID NO 430 NM_000100 SEQ ID NO 431 NM_000101 SEQ ID NO 432 NM_000104 SEQ ID NO 433 NM_000109 SEQ ID NO 434 NM_000125 SEQ ID NO 435 NM_000127 SEQ ID NO 436 NM_000135 SEQ ID NO 437 NM_000137 SEQ ID NO 438 NM_000146 SEQ ID NO 439 NM_000149 SEQ ID NO 440 NM_000154 SEQ ID NO 441 NM_000161 SEQ ID NO 443 NM_000165 SEQ ID NO 444 NM_000168 SEQ ID NO 445 NM_000169 SEQ ID NO 446 NM_000175 SEQ ID NO 447 NM_000191 SEQ ID NO 448 NM_000201 SEQ ID NO 450 NM_000211 SEQ ID NO 451 NM_000213 SEQ ID NO 452 NM_000224 SEQ ID NO 453 NM_000239 SEQ ID NO 454 NM_000251 SEQ ID NO 455 NM_000268 SEQ ID NO 456 NM_000270 SEQ ID NO 458 NM_000271 SEQ ID NO 459 NM_000283 SEQ ID NO 460 NM_000284 SEQ ID NO 461 NM_000286 SEQ ID NO 462 NM_000291 SEQ ID NO 463 NM_000299 SEQ ID NO 464 NM_000300 SEQ ID NO 465 NM_000310 SEQ ID NO 466 NM_000311 SEQ ID NO 467 NM_000317 SEQ ID NO 468 NM_000320 SEQ ID NO 469 NM_000342 SEQ ID NO 470 NM_000346 SEQ ID NO 471 NM_000352 SEQ ID NO 472 NM_000355 SEQ ID NO 473 NM_000358 SEQ ID NO 474 NM_000359 SEQ ID NO 475 NM_000362 SEQ ID NO 476 NM_000365 SEQ ID NO 477 NM_000381 SEQ ID NO 478 NM_000397 SEQ ID NO 480 NM_000399 SEQ ID NO 481 NM_000414 SEQ ID NO 482 NM_000416 SEQ ID NO 483 NM_000422 SEQ ID NO 484 NM_000424 SEQ ID NO 485 NM_000433 SEQ ID NO 486 NM_000436 SEQ ID NO 487 NM_000450 SEQ ID NO 488 NM_000462 SEQ ID NO 489 NM_000495 SEQ ID NO 490 NM_000507 SEQ ID NO 491 NM_000526 SEQ ID NO 492 NM_000557 SEQ ID NO 493 NM_000560 SEQ ID NO 494 NM_000576 SEQ ID NO 495 NM_000579 SEQ ID NO 496 NM_000584 SEQ ID NO 497 NM_000591 SEQ ID NO 498 NM_000592 SEQ ID NO 499 NM_000593 SEQ ID NO 500 NM_000594 SEQ ID NO 501 NM_000597 SEQ ID NO 502 NM_000600 SEQ ID NO 504 NM_000607 SEQ ID NO 505 NM_000612 SEQ ID NO 506 NM_000627 SEQ ID NO 507 NM_000633 SEQ ID NO 508 NM_000636 SEQ ID NO 509 NM_000639 SEQ ID NO 510 NM_000647 SEQ ID NO 511 NM_000655 SEQ ID NO 512 NM_000662 SEQ ID NO 513 NM_000663 SEQ ID NO 514 NM_000666 SEQ ID NO 515 NM_000676 SEQ ID NO 516 NM_000685 SEQ ID NO 517 NM_000693 SEQ ID NO 518 NM_000699 SEQ ID NO 519 NM_000700 SEQ ID NO 520 NM_000712 SEQ ID NO 521 NM_000727 SEQ ID NO 522 NM_000732 SEQ ID NO 523 NM_000734 SEQ ID NO 524 NM_000767 SEQ ID NO 525 NM_000784 SEQ ID NO 526 NM_000802 SEQ ID NO 528 NM_000824 SEQ ID NO 529 NM_000849 SEQ ID NO 530 NM_000852 SEQ ID NO 531 NM_000874 SEQ ID NO 532 NM_000878 SEQ ID NO 533 NM_000884 SEQ ID NO 534 NM_000908 SEQ ID NO 537 NM_000909 SEQ ID NO 538 NM_000926 SEQ ID NO 539 NM_000930 SEQ ID NO 540 NM_000931 SEQ ID NO 541 NM_000947 SEQ ID NO 542 NM_000949 SEQ ID NO 543 NM_000950 SEQ ID NO 544 NM_000954 SEQ ID NO 545 NM_000964 SEQ ID NO 546 NM_001003 SEQ ID NO 549 NM_001016 SEQ ID NO 551 NM_001047 SEQ ID NO 553 NM_001066 SEQ ID NO 555 NM_001071 SEQ ID NO 556 NM_001078 SEQ ID NO 557 NM_001085 SEQ ID NO 558 NM_001089 SEQ ID NO 559 NM_001109 SEQ ID NO 560 NM_001122 SEQ ID NO 561 NM_001124 SEQ ID NO 562 NM_001161 SEQ ID NO 563 NM_001165 SEQ ID NO 564 NM_001166 SEQ ID NO 565 NM_001168 SEQ ID NO 566 NM_001179 SEQ ID NO 567 NM_001185 SEQ ID NO 569 NM_001203 SEQ ID NO 570 NM_001207 SEQ ID NO 573 NM_001216 SEQ ID NO 574 NM_001218 SEQ ID NO 575 NM_001223 SEQ ID NO 576 NM_001225 SEQ ID NO 577 NM_001233 SEQ ID NO 578 NM_001236 SEQ ID NO 579 NM_001237 SEQ ID NO 580 NM_001251 SEQ ID NO 581 NM_001255 SEQ ID NO 582 NM_001262 SEQ ID NO 583 NM_001263 SEQ ID NO 584 NM_001267 SEQ ID NO 585 NM_001276 SEQ ID NO 587 NM_001280 SEQ ID NO 588 NM_001282 SEQ ID NO 589 NM_001295 SEQ ID NO 590 NM_001305 SEQ ID NO 591 NM_001310 SEQ ID NO 592 NM_001312 SEQ ID NO 593 NM_001321 SEQ ID NO 594 NM_001327 SEQ ID NO 595 NM_001329 SEQ ID NO 596 NM_001333 SEQ ID NO 597 NM_001338 SEQ ID NO 598 NM_001360 SEQ ID NO 599 NM_001363 SEQ ID NO 600 NM_001381 SEQ ID NO 601 NM_001394 SEQ ID NO 602 NM_001395 SEQ ID NO 603 NM_001419 SEQ ID NO 604 NM_001424 SEQ ID NO 605 NM_001428 SEQ ID NO 606 NM_001436 SEQ ID NO 607 NM_001444 SEQ ID NO 608 NM_001446 SEQ ID NO 609 NM_001453 SEQ ID NO 611 NM_001456 SEQ ID NO 612 NM_001457 SEQ ID NO 613 NM_001463 SEQ ID NO 614 NM_001465 SEQ ID NO 615 NM_001481 SEQ ID NO 616 NM_001493 SEQ ID NO 617 NM_001494 SEQ ID NO 618 NM_001500 SEQ ID NO 619 NM_001504 SEQ ID NO 620 NM_001511 SEQ ID NO 621 NM_001513 SEQ ID NO 622 NM_001527 SEQ ID NO 623 NM_001529 SEQ ID NO 624 NM_001530 SEQ ID NO 625 NM_001540 SEQ ID NO 626 NM_001550 SEQ ID NO 627 NM_001551 SEQ ID NO 628 NM_001552 SEQ ID NO 629 NM_001554 SEQ ID NO 631 NM_001558 SEQ ID NO 632 NM_001560 SEQ ID NO 633 NM_001565 SEQ ID NO 634 NM_001569 SEQ ID NO 635 NM_001605 SEQ ID NO 636 NM_001609 SEQ ID NO 637 NM_001615 SEQ ID NO 638 NM_001623 SEQ ID NO 639 NM_001627 SEQ ID NO 640 NM_001628 SEQ ID NO 641 NM_001630 SEQ ID NO 642 NM_001634 SEQ ID NO 643 NM_001656 SEQ ID NO 644 NM_001673 SEQ ID NO 645 NM_001675 SEQ ID NO 647 NM_001679 SEQ ID NO 648 NM_001689 SEQ ID NO 649 NM_001703 SEQ ID NO 650 NM_001710 SEQ ID NO 651 NM_001725 SEQ ID NO 652 NM_001730 SEQ ID NO 653 NM_001733 SEQ ID NO 654 NM_001734 SEQ ID NO 655 NM_001740 SEQ ID NO 656 NM_001745 SEQ ID NO 657 NM_001747 SEQ ID NO 658 NM_001756 SEQ ID NO 659 NM_001757 SEQ ID NO 660 NM_001758 SEQ ID NO 661 NM_001762 SEQ ID NO 662 NM_001767 SEQ ID NO 663 NM_001770 SEQ ID NO 664 NM_001777 SEQ ID NO 665 NM_001778 SEQ ID NO 666 NM_001781 SEQ ID NO 667 NM_001786 SEQ ID NO 668 NM_001793 SEQ ID NO 669 NM_001803 SEQ ID NO 671 NM_001806 SEQ ID NO 672 NM_001809 SEQ ID NO 673 NM_001814 SEQ ID NO 674 NM_001826 SEQ ID NO 675 NM_001830 SEQ ID NO 677 NM_001838 SEQ ID NO 678 NM_001839 SEQ ID NO 679 NM_001853 SEQ ID NO 681 NM_001859 SEQ ID NO 682 NM_001861 SEQ ID NO 683 NM_001874 SEQ ID NO 685 NM_001885 SEQ ID NO 686 NM_001892 SEQ ID NO 688 NM_001897 SEQ ID NO 689 NM_001899 SEQ ID NO 690 NM_001905 SEQ ID NO 691 NM_001912 SEQ ID NO 692 NM_001914 SEQ ID NO 693 NM_001919 SEQ ID NO 694 NM_001941 SEQ ID NO 695 NM_001943 SEQ ID NO 696 NM_001944 SEQ ID NO 697 NM_001953 SEQ ID NO 699 NM_001954 SEQ ID NO 700 NM_001955 SEQ ID NO 701 NM_001956 SEQ ID NO 702 NM_001958 SEQ ID NO 703 NM_001961 SEQ ID NO 705 NM_001970 SEQ ID NO 706 NM_001979 SEQ ID NO 707 NM_001982 SEQ ID NO 708 NM_002017 SEQ ID NO 710 NM_002033 SEQ ID NO 713 NM_002046 SEQ ID NO 714 NM_002047 SEQ ID NO 715 NM_002051 SEQ ID NO 716 NM_002053 SEQ ID NO 717 NM_002061 SEQ ID NO 718 NM_002065 SEQ ID NO 719 NM_002068 SEQ ID NO 720 NM_002077 SEQ ID NO 722 NM_002091 SEQ ID NO 723 NM_002101 SEQ ID NO 724 NM_002106 SEQ ID NO 725 NM_002110 SEQ ID NO 726 NM_002111 SEQ ID NO 727 NM_002115 SEQ ID NO 728 NM_002118 SEQ ID NO 729 NM_002123 SEQ ID NO 730 NM_002131 SEQ ID NO 731 NM_002136 SEQ ID NO 732 NM_002145 SEQ ID NO 733 NM_002164 SEQ ID NO 734 NM_002168 SEQ ID NO 735 NM_002184 SEQ ID NO 736 NM_002185 SEQ ID NO 737 NM_002189 SEQ ID NO 738 NM_002200 SEQ ID NO 739 NM_002201 SEQ ID NO 740 NM_002213 SEQ ID NO 741 NM_002219 SEQ ID NO 742 NM_002222 SEQ ID NO 743 NM_002239 SEQ ID NO 744 NM_002243 SEQ ID NO 745 NM_002245 SEQ ID NO 746 NM_002250 SEQ ID NO 747 NM_002254 SEQ ID NO 748 NM_002266 SEQ ID NO 749 NM_002273 SEQ ID NO 750 NM_002281 SEQ ID NO 751 NM_002292 SEQ ID NO 752 NM_002298 SEQ ID NO 753 NM_002300 SEQ ID NO 754 NM_002308 SEQ ID NO 755 NM_002314 SEQ ID NO 756 NM_002337 SEQ ID NO 757 NM_002341 SEQ ID NO 758 NM_002342 SEQ ID NO 759 NM_002346 SEQ ID NO 760 NM_002349 SEQ ID NO 761 NM_002350 SEQ ID NO 762 NM_002356 SEQ ID NO 763 NM_002358 SEQ ID NO 764 NM_002370 SEQ ID NO 765 NM_002395 SEQ ID NO 766 NM_002416 SEQ ID NO 767 NM_002421 SEQ ID NO 768 NM_002426 SEQ ID NO 769 NM_002435 SEQ ID NO 770 NM_002438 SEQ ID NO 771 NM_002444 SEQ ID NO 772 NM_002449 SEQ ID NO 773 NM_002450 SEQ ID NO 774 NM_002456 SEQ ID NO 775 NM_002466 SEQ ID NO 776 NM_002482 SEQ ID NO 777 NM_002497 SEQ ID NO 778 NM_002510 SEQ ID NO 779 NM_002515 SEQ ID NO 781 NM_002524 SEQ ID NO 782 NM_002539 SEQ ID NO 783 NM_002555 SEQ ID NO 785 NM_002570 SEQ ID NO 787 NM_002579 SEQ ID NO 788 NM_002587 SEQ ID NO 789 NM_002590 SEQ ID NO 790 NM_002600 SEQ ID NO 791 NM_002614 SEQ ID NO 792 NM_002618 SEQ ID NO 794 NM_002626 SEQ ID NO 795 NM_002633 SEQ ID NO 796 NM_002639 SEQ ID NO 797 NM_002648 SEQ ID NO 798 NM_002659 SEQ ID NO 799 NM_002661 SEQ ID NO 800 NM_002662 SEQ ID NO 801 NM_002664 SEQ ID NO 802 NM_002689 SEQ ID NO 804 NM_002690 SEQ ID NO 805 NM_002709 SEQ ID NO 806 NM_002727 SEQ ID NO 807 NM_002729 SEQ ID NO 808 NM_002734 SEQ ID NO 809 NM_002736 SEQ ID NO 810 NM_002740 SEQ ID NO 811 NM_002748 SEQ ID NO 813 NM_002774 SEQ ID NO 814 NM_002775 SEQ ID NO 815 NM_002776 SEQ ID NO 816 NM_002789 SEQ ID NO 817 NM_002794 SEQ ID NO 818 NM_002796 SEQ ID NO 819 NM_002800 SEQ ID NO 820 NM_002801 SEQ ID NO 821 NM_002808 SEQ ID NO 822 NM_002821 SEQ ID NO 824 NM_002826 SEQ ID NO 825 NM_002827 SEQ ID NO 826 NM_002838 SEQ ID NO 827 NM_002852 SEQ ID NO 828 NM_002854 SEQ ID NO 829 NM_002856 SEQ ID NO 830 NM_002857 SEQ ID NO 831 NM_002858 SEQ ID NO 832 NM_002888 SEQ ID NO 833 NM_002890 SEQ ID NO 834 NM_002901 SEQ ID NO 836 NM_002906 SEQ ID NO 837 NM_002916 SEQ ID NO 838 NM_002923 SEQ ID NO 839 NM_002933 SEQ ID NO 840 NM_002936 SEQ ID NO 841 NM_002937 SEQ ID NO 842 NM_002950 SEQ ID NO 843 NM_002961 SEQ ID NO 844 NM_002964 SEQ ID NO 845 NM_002965 SEQ ID NO 846 NM_002966 SEQ ID NO 847 NM_002982 SEQ ID NO 849 NM_002983 SEQ ID NO 850 NM_002984 SEQ ID NO 851 NM_002985 SEQ ID NO 852 NM_002988 SEQ ID NO 853 NM_002996 SEQ ID NO 854 NM_002997 SEQ ID NO 855 NM_002999 SEQ ID NO 856 NM_003012 SEQ ID NO 857 NM_003022 SEQ ID NO 858 NM_003034 SEQ ID NO 859 NM_003035 SEQ ID NO 860 NM_003039 SEQ ID NO 861 NM_003051 SEQ ID NO 862 NM_003064 SEQ ID NO 863 NM_003066 SEQ ID NO 864 NM_003088 SEQ ID NO 865 NM_003090 SEQ ID NO 866 NM_003096 SEQ ID NO 867 NM_003099 SEQ ID NO 868 NM_003102 SEQ ID NO 869 NM_003104 SEQ ID NO 870 NM_003108 SEQ ID NO 871 NM_003121 SEQ ID NO 873 NM_003134 SEQ ID NO 874 NM_003137 SEQ ID NO 875 NM_003144 SEQ ID NO 876 NM_003146 SEQ ID NO 877 NM_003149 SEQ ID NO 878 NM_003151 SEQ ID NO 879 NM_003157 SEQ ID NO 880 NM_003158 SEQ ID NO 881 NM_003165 SEQ ID NO 882 NM_003172 SEQ ID NO 883 NM_003177 SEQ ID NO 884 NM_003197 SEQ ID NO 885 NM_003202 SEQ ID NO 886 NM_003213 SEQ ID NO 887 NM_003217 SEQ ID NO 888 NM_003225 SEQ ID NO 889 NM_003226 SEQ ID NO 890 NM_003236 SEQ ID NO 892 NM_003239 SEQ ID NO 893 NM_003248 SEQ ID NO 894 NM_003255 SEQ ID NO 895 NM_003258 SEQ ID NO 896 NM_003264 SEQ ID NO 897 NM_003283 SEQ ID NO 898 NM_003318 SEQ ID NO 899 NM_003329 SEQ ID NO 900 NM_003332 SEQ ID NO 901 NM_003358 SEQ ID NO 902 NM_003359 SEQ ID NO 903 NM_003360 SEQ ID NO 904 NM_003368 SEQ ID NO 905 NM_003376 SEQ ID NO 906 NM_003380 SEQ ID NO 907 NM_003392 SEQ ID NO 908 NM_003412 SEQ ID NO 909 NM_003430 SEQ ID NO 910 NM_003462 SEQ ID NO 911 NM_003467 SEQ ID NO 912 NM_003472 SEQ ID NO 913 NM_003479 SEQ ID NO 914 NM_003489 SEQ ID NO 915 NM_003494 SEQ ID NO 916 NM_003498 SEQ ID NO 917 NM_003504 SEQ ID NO 919 NM_003508 SEQ ID NO 920 NM_003510 SEQ ID NO 921 NM_003512 SEQ ID NO 922 NM_003528 SEQ ID NO 923 NM_003544 SEQ ID NO 924 NM_003561 SEQ ID NO 925 NM_003563 SEQ ID NO 926 NM_003568 SEQ ID NO 927 NM_003579 SEQ ID NO 928 NM_003600 SEQ ID NO 929 NM_003615 SEQ ID NO 931 NM_003627 SEQ ID NO 932 NM_003645 SEQ ID NO 935 NM_003651 SEQ ID NO 936 NM_003657 SEQ ID NO 937 NM_003662 SEQ ID NO 938 NM_003670 SEQ ID NO 939 NM_003675 SEQ ID NO 940 NM_003676 SEQ ID NO 941 NM_003681 SEQ ID NO 942 NM_003683 SEQ ID NO 943 NM_003686 SEQ ID NO 944 NM_003689 SEQ ID NO 945 NM_003714 SEQ ID NO 946 NM_003720 SEQ ID NO 947 NM_003726 SEQ ID NO 948 NM_003729 SEQ ID NO 949 NM_003740 SEQ ID NO 950 NM_003772 SEQ ID NO 952 NM_003791 SEQ ID NO 953 NM_003793 SEQ ID NO 954 NM_003795 SEQ ID NO 955 NM_003806 SEQ ID NO 956 NM_003821 SEQ ID NO 957 NM_003829 SEQ ID NO 958 NM_003831 SEQ ID NO 959 NM_003862 SEQ ID NO 960 NM_003866 SEQ ID NO 961 NM_003875 SEQ ID NO 962 NM_003878 SEQ ID NO 963 NM_003894 SEQ ID NO 965 NM_003897 SEQ ID NO 966 NM_003904 SEQ ID NO 967 NM_003929 SEQ ID NO 968 NM_003933 SEQ ID NO 969 NM_003937 SEQ ID NO 970 NM_003940 SEQ ID NO 971 NM_003942 SEQ ID NO 972 NM_003944 SEQ ID NO 973 NM_003953 SEQ ID NO 974 NM_003954 SEQ ID NO 975 NM_003975 SEQ ID NO 976 NM_003981 SEQ ID NO 977 NM_003982 SEQ ID NO 978 NM_003986 SEQ ID NO 979 NM_004003 SEQ ID NO 980 NM_004010 SEQ ID NO 981 NM_004024 SEQ ID NO 982 NM_004038 SEQ ID NO 983 NM_004049 SEQ ID NO 984 NM_004052 SEQ ID NO 985 NM_004053 SEQ ID NO 986 NM_004079 SEQ ID NO 987 NM_004104 SEQ ID NO 988 NM_004109 SEQ ID NO 989 NM_004110 SEQ ID NO 990 NM_004120 SEQ ID NO 991 NM_004131 SEQ ID NO 992 NM_004143 SEQ ID NO 993 NM_004154 SEQ ID NO 994 NM_004170 SEQ ID NO 996 NM_004172 SEQ ID NO 997 NM_004176 SEQ ID NO 998 NM_004180 SEQ ID NO 999 NM_004181 SEQ ID NO 1000 NM_004184 SEQ ID NO 1001 NM_004203 SEQ ID NO 1002 NM_004207 SEQ ID NO 1003 NM_004217 SEQ ID NO 1004 NM_004219 SEQ ID NO 1005 NM_004221 SEQ ID NO 1006 NM_004233 SEQ ID NO 1007 NM_004244 SEQ ID NO 1008 NM_004252 SEQ ID NO 1009 NM_004265 SEQ ID NO 1010 NM_004267 SEQ ID NO 1011 NM_004281 SEQ ID NO 1012 NM_004289 SEQ ID NO 1013 NM_004298 SEQ ID NO 1015 NM_004301 SEQ ID NO 1016 NM_004305 SEQ ID NO 1017 NM_004311 SEQ ID NO 1018 NM_004315 SEQ ID NO 1019 NM_004323 SEQ ID NO 1020 NM_004330 SEQ ID NO 1021 NM_004336 SEQ ID NO 1022 NM_004338 SEQ ID NO 1023 NM_004350 SEQ ID NO 1024 NM_004354 SEQ ID NO 1025 NM_004358 SEQ ID NO 1026 NM_004360 SEQ ID NO 1027 NM_004362 SEQ ID NO 1028 NM_004374 SEQ ID NO 1029 NM_004378 SEQ ID NO 1030 NM_004392 SEQ ID NO 1031 NM_004395 SEQ ID NO 1032 NM_004414 SEQ ID NO 1033 NM_004418 SEQ ID NO 1034 NM_004425 SEQ ID NO 1035 NM_004431 SEQ ID NO 1036 NM_004436 SEQ ID NO 1037 NM_004438 SEQ ID NO 1038 NM_004443 SEQ ID NO 1039 NM_004446 SEQ ID NO 1040 NM_004451 SEQ ID NO 1041 NM_004454 SEQ ID NO 1042 NM_004456 SEQ ID NO 1043 NM_004458 SEQ ID NO 1044 NM_004472 SEQ ID NO 1045 NM_004480 SEQ ID NO 1046 NM_004482 SEQ ID NO 1047 NM_004494 SEQ ID NO 1048 NM_004496 SEQ ID NO 1049 NM_004503 SEQ ID NO 1050 NM_004504 SEQ ID NO 1051 NM_004515 SEQ ID NO 1052 NM_004522 SEQ ID NO 1053 NM_004523 SEQ ID NO 1054 NM_004525 SEQ ID NO 1055 NM_004556 SEQ ID NO 1056 NM_004559 SEQ ID NO 1057 NM_004569 SEQ ID NO 1058 NM_004577 SEQ ID NO 1059 NM_004585 SEQ ID NO 1060 NM_004587 SEQ ID NO 1061 NM_004594 SEQ ID NO 1062 NM_004599 SEQ ID NO 1063 NM_004633 SEQ ID NO 1066 NM_004642 SEQ ID NO 1067 NM_004648 SEQ ID NO 1068 NM_004663 SEQ ID NO 1069 NM_004664 SEQ ID NO 1070 NM_004684 SEQ ID NO 1071 NM_004688 SEQ ID NO 1072 NM_004694 SEQ ID NO 1073 NM_004695 SEQ ID NO 1074 NM_004701 SEQ ID NO 1075 NM_004708 SEQ ID NO 1077 NM_004711 SEQ ID NO 1078 NM_004726 SEQ ID NO 1079 NM_004750 SEQ ID NO 1081 NM_004761 SEQ ID NO 1082 NM_004762 SEQ ID NO 1083 NM_004780 SEQ ID NO 1085 NM_004791 SEQ ID NO 1086 NM_004798 SEQ ID NO 1087 NM_004808 SEQ ID NO 1088 NM_004811 SEQ ID NO 1089 NM_004833 SEQ ID NO 1090 NM_004835 SEQ ID NO 1091 NM_004843 SEQ ID NO 1092 NM_004847 SEQ ID NO 1093 NM_004848 SEQ ID NO 1094 NM_004864 SEQ ID NO 1095 NM_004865 SEQ ID NO 1096 NM_004866 SEQ ID NO 1097 NM_004877 SEQ ID NO 1098 NM_004900 SEQ ID NO 1099 NM_004906 SEQ ID NO 1100 NM_004910 SEQ ID NO 1101 NM_004918 SEQ ID NO 1103 NM_004923 SEQ ID NO 1104 NM_004938 SEQ ID NO 1105 NM_004951 SEQ ID NO 1106 NM_004968 SEQ ID NO 1107 NM_004994 SEQ ID NO 1108 NM_004999 SEQ ID NO 1109 NM_005001 SEQ ID NO 1110 NM_005002 SEQ ID NO 1111 NM_005012 SEQ ID NO 1112 NM_005032 SEQ ID NO 1113 NM_005044 SEQ ID NO 1114 NM_005046 SEQ ID NO 1115 NM_005049 SEQ ID NO 1116 NM_005067 SEQ ID NO 1117 NM_005077 SEQ ID NO 1118 NM_005080 SEQ ID NO 1119 NM_005084 SEQ ID NO 1120 NM_005130 SEQ ID NO 1122 NM_005139 SEQ ID NO 1123 NM_005168 SEQ ID NO 1125 NM_005190 SEQ ID NO 1126 NM_005196 SEQ ID NO 1127 NM_005213 SEQ ID NO 1128 NM_005218 SEQ ID NO 1129 NM_005235 SEQ ID NO 1130 NM_005245 SEQ ID NO 1131 NM_005249 SEQ ID NO 1132 NM_005257 SEQ ID NO 1133 NM_005264 SEQ ID NO 1134 NM_005271 SEQ ID NO 1135 NM_005314 SEQ ID NO 1136 NM_005321 SEQ ID NO 1137 NM_005322 SEQ ID NO 1138 NM_005325 SEQ ID NO 1139 NM_005326 SEQ ID NO 1140 NM_005335 SEQ ID NO 1141 NM_005337 SEQ ID NO 1142 NM_005342 SEQ ID NO 1143 NM_005345 SEQ ID NO 1144 NM_005357 SEQ ID NO 1145 NM_005375 SEQ ID NO 1146 NM_005391 SEQ ID NO 1147 NM_005408 SEQ ID NO 1148 NM_005409 SEQ ID NO 1149 NM_005410 SEQ ID NO 1150 NM_005426 SEQ ID NO 1151 NM_005433 SEQ ID NO 1152 NM_005441 SEQ ID NO 1153 NM_005443 SEQ ID NO 1154 NM_005483 SEQ ID NO 1155 NM_005486 SEQ ID NO 1156 NM_005496 SEQ ID NO 1157 NM_005498 SEQ ID NO 1158 NM_005499 SEQ ID NO 1159 NM_005514 SEQ ID NO 1160 NM_005531 SEQ ID NO 1162 NM_005538 SEQ ID NO 1163 NM_005541 SEQ ID NO 1164 NM_005544 SEQ ID NO 1165 NM_005548 SEQ ID NO 1166 NM_005554 SEQ ID NO 1167 NM_005555 SEQ ID NO 1168 NM_005556 SEQ ID NO 1169 NM_005557 SEQ ID NO 1170 NM_005558 SEQ ID NO 1171 NM_005562 SEQ ID NO 1172 NM_005563 SEQ ID NO 1173 NM_005565 SEQ ID NO 1174 NM_005566 SEQ ID NO 1175 NM_005572 SEQ ID NO 1176 NM_005582 SEQ ID NO 1177 NM_005608 SEQ ID NO 1178 NM_005614 SEQ ID NO 1179 NM_005617 SEQ ID NO 1180 NM_005620 SEQ ID NO 1181 NM_005625 SEQ ID NO 1182 NM_005651 SEQ ID NO 1183 NM_005658 SEQ ID NO 1184 NM_005659 SEQ ID NO 1185 NM_005667 SEQ ID NO 1186 NM_005686 SEQ ID NO 1187 NM_005690 SEQ ID NO 1188 NM_005720 SEQ ID NO 1190 NM_005727 SEQ ID NO 1191 NM_005733 SEQ ID NO 1192 NM_005737 SEQ ID NO 1193 NM_005742 SEQ ID NO 1194 NM_005746 SEQ ID NO 1195 NM_005749 SEQ ID NO 1196 NM_005760 SEQ ID NO 1197 NM_005764 SEQ ID NO 1198 NM_005794 SEQ ID NO 1199 NM_005796 SEQ ID NO 1200 NM_005804 SEQ ID NO 1201 NM_005813 SEQ ID NO 1202 NM_005824 SEQ ID NO 1203 NM_005825 SEQ ID NO 1204 NM_005849 SEQ ID NO 1205 NM_005853 SEQ ID NO 1206 NM_005855 SEQ ID NO 1207 NM_005864 SEQ ID NO 1208 NM_005874 SEQ ID NO 1209 NM_005876 SEQ ID NO 1210 NM_005880 SEQ ID NO 1211 NM_005891 SEQ ID NO 1212 NM_005892 SEQ ID NO 1213 NM_005899 SEQ ID NO 1214 NM_005915 SEQ ID NO 1215 NM_005919 SEQ ID NO 1216 NM_005923 SEQ ID NO 1217 NM_005928 SEQ ID NO 1218 NM_005932 SEQ ID NO 1219 NM_005935 SEQ ID NO 1220 NM_005945 SEQ ID NO 1221 NM_005953 SEQ ID NO 1222 NM_005978 SEQ ID NO 1223 NM_005990 SEQ ID NO 1224 NM_006002 SEQ ID NO 1225 NM_006004 SEQ ID NO 1226 NM_006005 SEQ ID NO 1227 NM_006006 SEQ ID NO 1228 NM_006017 SEQ ID NO 1229 NM_006018 SEQ ID NO 1230 NM_006023 SEQ ID NO 1231 NM_006027 SEQ ID NO 1232 NM_006029 SEQ ID NO 1233 NM_006033 SEQ ID NO 1234 NM_006051 SEQ ID NO 1235 NM_006055 SEQ ID NO 1236 NM_006074 SEQ ID NO 1237 NM_006086 SEQ ID NO 1238 NM_006087 SEQ ID NO 1239 NM_006096 SEQ ID NO 1240 NM_006101 SEQ ID NO 1241 NM_006103 SEQ ID NO 1242 NM_006111 SEQ ID NO 1243 NM_006113 SEQ ID NO 1244 NM_006115 SEQ ID NO 1245 NM_006117 SEQ ID NO 1246 NM_006142 SEQ ID NO 1247 NM_006144 SEQ ID NO 1248 NM_006148 SEQ ID NO 1249 NM_006153 SEQ ID NO 1250 NM_006159 SEQ ID NO 1251 NM_006170 SEQ ID NO 1252 NM_006197 SEQ ID NO 1253 NM_006224 SEQ ID NO 1255 NM_006227 SEQ ID NO 1256 NM_006235 SEQ ID NO 1257 NM_006243 SEQ ID NO 1258 NM_006264 SEQ ID NO 1259 NM_006271 SEQ ID NO 1261 NM_006274 SEQ ID NO 1262 NM_006290 SEQ ID NO 1265 NM_006291 SEQ ID NO 1266 NM_006296 SEQ ID NO 1267 NM_006304 SEQ ID NO 1268 NM_006314 SEQ ID NO 1269 NM_006332 SEQ ID NO 1270 NM_006357 SEQ ID NO 1271 NM_006366 SEQ ID NO 1272 NM_006372 SEQ ID NO 1273 NM_006377 SEQ ID NO 1274 NM_006378 SEQ ID NO 1275 NM_006383 SEQ ID NO 1276 NM_006389 SEQ ID NO 1277 NM_006393 SEQ ID NO 1278 NM_006398 SEQ ID NO 1279 NM_006406 SEQ ID NO 1280 NM_006408 SEQ ID NO 1281 NM_006410 SEQ ID NO 1282 NM_006414 SEQ ID NO 1283 NM_006417 SEQ ID NO 1284 NM_006430 SEQ ID NO 1285 NM_006460 SEQ ID NO 1286 NM_006461 SEQ ID NO 1287 NM_006469 SEQ ID NO 1288 NM_006470 SEQ ID NO 1289 NM_006491 SEQ ID NO 1290 NM_006495 SEQ ID NO 1291 NM_006500 SEQ ID NO 1292 NM_006509 SEQ ID NO 1293 NM_006516 SEQ ID NO 1294 NM_006533 SEQ ID NO 1295 NM_006551 SEQ ID NO 1296 NM_006556 SEQ ID NO 1297 NM_006558 SEQ ID NO 1298 NM_006564 SEQ ID NO 1299 NM_006573 SEQ ID NO 1300 NM_006607 SEQ ID NO 1301 NM_006622 SEQ ID NO 1302 NM_006623 SEQ ID NO 1303 NM_006636 SEQ ID NO 1304 NM_006670 SEQ ID NO 1305 NM_006681 SEQ ID NO 1306 NM_006682 SEQ ID NO 1307 NM_006696 SEQ ID NO 1308 NM_006698 SEQ ID NO 1309 NM_006705 SEQ ID NO 1310 NM_006739 SEQ ID NO 1311 NM_006748 SEQ ID NO 1312 NM_006759 SEQ ID NO 1313 NM_006762 SEQ ID NO 1314 NM_006763 SEQ ID NO 1315 NM_006769 SEQ ID NO 1316 NM_006770 SEQ ID NO 1317 NM_006780 SEQ ID NO 1318 NM_006787 SEQ ID NO 1319 NM_006806 SEQ ID NO 1320 NM_006813 SEQ ID NO 1321 NM_006825 SEQ ID NO 1322 NM_006826 SEQ ID NO 1323 NM_006829 SEQ ID NO 1324 NM_006834 SEQ ID NO 1325 NM_006835 SEQ ID NO 1326 NM_006840 SEQ ID NO 1327 NM_006845 SEQ ID NO 1328 NM_006847 SEQ ID NO 1329 NM_006851 SEQ ID NO 1330 NM_006855 SEQ ID NO 1331 NM_006864 SEQ ID NO 1332 NM_006868 SEQ ID NO 1333 NM_006875 SEQ ID NO 1334 NM_006889 SEQ ID NO 1336 NM_006892 SEQ ID NO 1337 NM_006912 SEQ ID NO 1338 NM_006931 SEQ ID NO 1341 NM_006941 SEQ ID NO 1342 NM_006943 SEQ ID NO 1343 NM_006984 SEQ ID NO 1344 NM_007005 SEQ ID NO 1345 NM_007006 SEQ ID NO 1346 NM_007019 SEQ ID NO 1347 NM_007027 SEQ ID NO 1348 NM_007044 SEQ ID NO 1350 NM_007050 SEQ ID NO 1351 NM_007057 SEQ ID NO 1352 NM_007069 SEQ ID NO 1353 NM_007074 SEQ ID NO 1355 NM_007088 SEQ ID NO 1356 NM_007111 SEQ ID NO 1357 NM_007146 SEQ ID NO 1358 NM_007173 SEQ ID NO 1359 NM_007177 SEQ ID NO 1360 NM_007196 SEQ ID NO 1361 NM_007203 SEQ ID NO 1362 NM_007214 SEQ ID NO 1363 NM_007217 SEQ ID NO 1364 NM_007231 SEQ ID NO 1365 NM_007268 SEQ ID NO 1367 NM_007274 SEQ ID NO 1368 NM_007275 SEQ ID NO 1369 NM_007281 SEQ ID NO 1370 NM_007309 SEQ ID NO 1371 NM_007315 SEQ ID NO 1372 NM_007334 SEQ ID NO 1373 NM_007358 SEQ ID NO 1374 NM_009585 SEQ ID NO 1375 NM_009587 SEQ ID NO 1376 NM_009588 SEQ ID NO 1377 NM_012062 SEQ ID NO 1378 NM_012067 SEQ ID NO 1379 NM_012101 SEQ ID NO 1380 NM_012105 SEQ ID NO 1381 NM_012108 SEQ ID NO 1382 NM_012110 SEQ ID NO 1383 NM_012124 SEQ ID NO 1384 NM_012142 SEQ ID NO 1386 NM_012155 SEQ ID NO 1388 NM_012175 SEQ ID NO 1389 NM_012177 SEQ ID NO 1390 NM_012205 SEQ ID NO 1391 NM_012219 SEQ ID NO 1393 NM_012242 SEQ ID NO 1394 NM_012250 SEQ ID NO 1395 NM_012261 SEQ ID NO 1397 NM_012286 SEQ ID NO 1398 NM_012319 SEQ ID NO 1400 NM_012332 SEQ ID NO 1401 NM_012336 SEQ ID NO 1402 NM_012339 SEQ ID NO 1404 NM_012341 SEQ ID NO 1405 NM_012391 SEQ ID NO 1406 NM_012394 SEQ ID NO 1407 NM_012413 SEQ ID NO 1408 NM_012421 SEQ ID NO 1409 NM_012425 SEQ ID NO 1410 NM_012427 SEQ ID NO 1411 NM_012429 SEQ ID NO 1413 NM_012446 SEQ ID NO 1414 NM_012463 SEQ ID NO 1415 NM_012474 SEQ ID NO 1416 NM_013230 SEQ ID NO 1417 NM_013233 SEQ ID NO 1418 NM_013238 SEQ ID NO 1419 NM_013239 SEQ ID NO 1420 NM_013242 SEQ ID NO 1421 NM_013257 SEQ ID NO 1423 NM_013261 SEQ ID NO 1424 NM_013262 SEQ ID NO 1425 NM_013277 SEQ ID NO 1426 NM_013296 SEQ ID NO 1427 NM_013301 SEQ ID NO 1428 NM_013324 SEQ ID NO 1429 NM_013327 SEQ ID NO 1430 NM_013336 SEQ ID NO 1431 NM_013339 SEQ ID NO 1432 NM_013363 SEQ ID NO 1433 NM_013378 SEQ ID NO 1435 NM_013384 SEQ ID NO 1436 NM_013385 SEQ ID NO 1437 NM_013406 SEQ ID NO 1438 NM_013437 SEQ ID NO 1439 NM_013451 SEQ ID NO 1440 NM_013943 SEQ ID NO 1441 NM_013994 SEQ ID NO 1442 NM_013995 SEQ ID NO 1443 NM_014026 SEQ ID NO 1444 NM_014029 SEQ ID NO 1445 NM_014036 SEQ ID NO 1446 NM_014062 SEQ ID NO 1447 NM_014074 SEQ ID NO 1448 NM_014096 SEQ ID NO 1450 NM_014109 SEQ ID NO 1451 NM_014112 SEQ ID NO 1452 NM_014147 SEQ ID NO 1453 NM_014149 SEQ ID NO 1454 NM_014164 SEQ ID NO 1455 NM_014172 SEQ ID NO 1456 NM_014175 SEQ ID NO 1457 NM_014181 SEQ ID NO 1458 NM_014184 SEQ ID NO 1459 NM_014211 SEQ ID NO 1460 NM_014214 SEQ ID NO 1461 NM_014216 SEQ ID NO 1462 NM_014241 SEQ ID NO 1463 NM_014246 SEQ ID NO 1465 NM_014268 SEQ ID NO 1466 NM_014272 SEQ ID NO 1467 NM_014274 SEQ ID NO 1468 NM_014289 SEQ ID NO 1469 NM_014298 SEQ ID NO 1470 NM_014302 SEQ ID NO 1471 NM_014315 SEQ ID NO 1473 NM_014316 SEQ ID NO 1474 NM_014317 SEQ ID NO 1475 NM_014320 SEQ ID NO 1476 NM_014321 SEQ ID NO 1477 NM_014325 SEQ ID NO 1478 NM_014335 SEQ ID NO 1479 NM_014363 SEQ ID NO 1480 NM_014364 SEQ ID NO 1481 NM_014365 SEQ ID NO 1482 NM_014373 SEQ ID NO 1483 NM_014382 SEQ ID NO 1484 NM_014395 SEQ ID NO 1485 NM_014398 SEQ ID NO 1486 NM_014399 SEQ ID NO 1487 NM_014402 SEQ ID NO 1488 NM_014428 SEQ ID NO 1489 NM_014448 SEQ ID NO 1490 NM_014449 SEQ ID NO 1491 NM_014450 SEQ ID NO 1492 NM_014452 SEQ ID NO 1493 NM_014453 SEQ ID NO 1494 NM_014456 SEQ ID NO 1495 NM_014479 SEQ ID NO 1497 NM_014501 SEQ ID NO 1498 NM_014552 SEQ ID NO 1500 NM_014553 SEQ ID NO 1501 NM_014570 SEQ ID NO 1502 NM_014575 SEQ ID NO 1503 NM_014585 SEQ ID NO 1504 NM_014595 SEQ ID NO 1505 NM_014624 SEQ ID NO 1507 NM_014633 SEQ ID NO 1508 NM_014640 SEQ ID NO 1509 NM_014642 SEQ ID NO 1510 NM_014643 SEQ ID NO 1511 NM_014656 SEQ ID NO 1512 NM_014668 SEQ ID NO 1513 NM_014669 SEQ ID NO 1514 NM_014673 SEQ ID NO 1515 NM_014675 SEQ ID NO 1516 NM_014679 SEQ ID NO 1517 NM_014680 SEQ ID NO 1518 NM_014696 SEQ ID NO 1519 NM_014700 SEQ ID NO 1520 NM_014715 SEQ ID NO 1521 NM_014721 SEQ ID NO 1522 NM_014737 SEQ ID NO 1524 NM_014738 SEQ ID NO 1525 NM_014747 SEQ ID NO 1526 NM_014750 SEQ ID NO 1527 NM_014754 SEQ ID NO 1528 NM_014767 SEQ ID NO 1529 NM_014770 SEQ ID NO 1530 NM_014773 SEQ ID NO 1531 NM_014776 SEQ ID NO 1532 NM_014782 SEQ ID NO 1533 NM_014785 SEQ ID NO 1534 NM_014791 SEQ ID NO 1535 NM_014808 SEQ ID NO 1536 NM_014811 SEQ ID NO 1537 NM_014812 SEQ ID NO 1538 NM_014838 SEQ ID NO 1540 NM_014862 SEQ ID NO 1542 NM_014865 SEQ ID NO 1543 NM_014870 SEQ ID NO 1544 NM_014875 SEQ ID NO 1545 NM_014886 SEQ ID NO 1547 NM_014889 SEQ ID NO 1548 NM_014905 SEQ ID NO 1549 NM_014935 SEQ ID NO 1550 NM_014945 SEQ ID NO 1551 NM_014965 SEQ ID NO 1552 NM_014967 SEQ ID NO 1553 NM_014968 SEQ ID NO 1554 NM_015032 SEQ ID NO 1555 NM_015239 SEQ ID NO 1556 NM_015383 SEQ ID NO 1557 NM_015392 SEQ ID NO 1558 NM_015416 SEQ ID NO 1559 NM_015417 SEQ ID NO 1560 NM_015420 SEQ ID NO 1561 NM_015434 SEQ ID NO 1562 NM_015474 SEQ ID NO 1563 NM_015507 SEQ ID NO 1565 NM_015513 SEQ ID NO 1566 NM_015515 SEQ ID NO 1567 NM_015523 SEQ ID NO 1568 NM_015524 SEQ ID NO 1569 NM_015599 SEQ ID NO 1571 NM_015623 SEQ ID NO 1572 NM_015640 SEQ ID NO 1573 NM_015641 SEQ ID NO 1574 NM_015678 SEQ ID NO 1575 NM_015721 SEQ ID NO 1576 NM_015892 SEQ ID NO 1578 NM_015895 SEQ ID NO 1579 NM_015907 SEQ ID NO 1580 NM_015925 SEQ ID NO 1581 NM_015937 SEQ ID NO 1582 NM_015954 SEQ ID NO 1583 NM_015955 SEQ ID NO 1584 NM_015961 SEQ ID NO 1585 NM_015984 SEQ ID NO 1587 NM_015986 SEQ ID NO 1588 NM_015987 SEQ ID NO 1589 NM_015991 SEQ ID NO 1590 NM_016002 SEQ ID NO 1592 NM_016028 SEQ ID NO 1594 NM_016029 SEQ ID NO 1595 NM_016047 SEQ ID NO 1596 NM_016048 SEQ ID NO 1597 NM_016050 SEQ ID NO 1598 NM_016056 SEQ ID NO 1599 NM_016058 SEQ ID NO 1600 NM_016066 SEQ ID NO 1601 NM_016072 SEQ ID NO 1602 NM_016073 SEQ ID NO 1603 NM_016108 SEQ ID NO 1605 NM_016109 SEQ ID NO 1606 NM_016121 SEQ ID NO 1607 NM_016126 SEQ ID NO 1608 NM_016127 SEQ ID NO 1609 NM_016135 SEQ ID NO 1610 NM_016142 SEQ ID NO 1612 NM_016153 SEQ ID NO 1613 NM_016171 SEQ ID NO 1614 NM_016175 SEQ ID NO 1615 NM_016184 SEQ ID NO 1616 NM_016185 SEQ ID NO 1617 NM_016187 SEQ ID NO 1618 NM_016199 SEQ ID NO 1619 NM_016210 SEQ ID NO 1620 NM_016217 SEQ ID NO 1621 NM_016228 SEQ ID NO 1623 NM_016229 SEQ ID NO 1624 NM_016235 SEQ ID NO 1625 NM_016240 SEQ ID NO 1626 NM_016243 SEQ ID NO 1627 NM_016250 SEQ ID NO 1628 NM_016267 SEQ ID NO 1629 NM_016271 SEQ ID NO 1630 NM_016299 SEQ ID NO 1631 NM_016306 SEQ ID NO 1632 NM_016308 SEQ ID NO 1634 NM_016321 SEQ ID NO 1635 NM_016337 SEQ ID NO 1636 NM_016352 SEQ ID NO 1637 NM_016359 SEQ ID NO 1638 NM_016401 SEQ ID NO 1641 NM_016403 SEQ ID NO 1642 NM_016411 SEQ ID NO 1643 NM_016423 SEQ ID NO 1644 NM_016463 SEQ ID NO 1647 NM_016475 SEQ ID NO 1649 NM_016477 SEQ ID NO 1650 NM_016491 SEQ ID NO 1651 NM_016495 SEQ ID NO 1652 NM_016542 SEQ ID NO 1653 NM_016548 SEQ ID NO 1654 NM_016569 SEQ ID NO 1655 NM_016577 SEQ ID NO 1656 NM_016582 SEQ ID NO 1657 NM_016593 SEQ ID NO 1658 NM_016603 SEQ ID NO 1659 NM_016612 SEQ ID NO 1660 NM_016619 SEQ ID NO 1661 NM_016623 SEQ ID NO 1663 NM_016625 SEQ ID NO 1664 NM_016629 SEQ ID NO 1665 NM_016640 SEQ ID NO 1666 NM_016645 SEQ ID NO 1667 NM_016650 SEQ ID NO 1668 NM_016657 SEQ ID NO 1669 NM_016733 SEQ ID NO 1670 NM_016815 SEQ ID NO 1671 NM_016817 SEQ ID NO 1672 NM_016818 SEQ ID NO 1673 NM_016839 SEQ ID NO 1675 NM_017414 SEQ ID NO 1676 NM_017422 SEQ ID NO 1677 NM_017423 SEQ ID NO 1678 NM_017447 SEQ ID NO 1679 NM_017518 SEQ ID NO 1680 NM_017522 SEQ ID NO 1681 NM_017540 SEQ ID NO 1682 NM_017555 SEQ ID NO 1683 NM_017572 SEQ ID NO 1684 NM_017585 SEQ ID NO 1685 NM_017586 SEQ ID NO 1686 NM_017596 SEQ ID NO 1687 NM_017606 SEQ ID NO 1688 NM_017617 SEQ ID NO 1689 NM_017633 SEQ ID NO 1690 NM_017634 SEQ ID NO 1691 NM_017646 SEQ ID NO 1692 NM_017660 SEQ ID NO 1693 NM_017680 SEQ ID NO 1694 NM_017691 SEQ ID NO 1695 NM_017698 SEQ ID NO 1696 NM_017702 SEQ ID NO 1697 NM_017731 SEQ ID NO 1699 NM_017732 SEQ ID NO 1700 NM_017733 SEQ ID NO 1701 NM_017734 SEQ ID NO 1702 NM_017746 SEQ ID NO 1703 NM_017750 SEQ ID NO 1704 NM_017761 SEQ ID NO 1705 NM_017763 SEQ ID NO 1706 NM_017770 SEQ ID NO 1707 NM_017779 SEQ ID NO 1708 NM_017780 SEQ ID NO 1709 NM_017782 SEQ ID NO 1710 NM_017786 SEQ ID NO 1711 NM_017791 SEQ ID NO 1712 NM_017805 SEQ ID NO 1713 NM_017816 SEQ ID NO 1714 NM_017821 SEQ ID NO 1715 NM_017835 SEQ ID NO 1716 NM_017843 SEQ ID NO 1717 NM_017857 SEQ ID NO 1718 NM_017901 SEQ ID NO 1719 NM_017906 SEQ ID NO 1720 NM_017918 SEQ ID NO 1721 NM_017961 SEQ ID NO 1722 NM_017996 SEQ ID NO 1723 NM_018000 SEQ ID NO 1724 NM_018004 SEQ ID NO 1725 NM_018011 SEQ ID NO 1726 NM_018014 SEQ ID NO 1727 NM_018022 SEQ ID NO 1728 NM_018031 SEQ ID NO 1729 NM_018043 SEQ ID NO 1730 NM_018048 SEQ ID NO 1731 NM_018062 SEQ ID NO 1732 NM_018069 SEQ ID NO 1733 NM_018072 SEQ ID NO 1734 NM_018077 SEQ ID NO 1735 NM_018086 SEQ ID NO 1736 NM_018087 SEQ ID NO 1737 NM_018093 SEQ ID NO 1738 NM_018098 SEQ ID NO 1739 NM_018099 SEQ ID NO 1740 NM_018101 SEQ ID NO 1741 NM_018103 SEQ ID NO 1742 NM_018109 SEQ ID NO 1744 NM_018123 SEQ ID NO 1746 NM_018131 SEQ ID NO 1747 NM_018136 SEQ ID NO 1748 NM_018138 SEQ ID NO 1749 NM_018166 SEQ ID NO 1750 NM_018171 SEQ ID NO 1751 NM_018178 SEQ ID NO 1752 NM_018181 SEQ ID NO 1753 NM_018186 SEQ ID NO 1754 NM_018188 SEQ ID NO 1756 NM_018194 SEQ ID NO 1757 NM_018204 SEQ ID NO 1758 NM_018208 SEQ ID NO 1759 NM_018212 SEQ ID NO 1760 NM_018234 SEQ ID NO 1763 NM_018255 SEQ ID NO 1764 NM_018257 SEQ ID NO 1765 NM_018265 SEQ ID NO 1766 NM_018271 SEQ ID NO 1767 NM_018290 SEQ ID NO 1768 NM_018295 SEQ ID NO 1769 NM_018304 SEQ ID NO 1770 NM_018306 SEQ ID NO 1771 NM_018326 SEQ ID NO 1772 NM_018346 SEQ ID NO 1773 NM_018366 SEQ ID NO 1775 NM_018370 SEQ ID NO 1776 NM_018373 SEQ ID NO 1777 NM_018379 SEQ ID NO 1778 NM_018384 SEQ ID NO 1779 NM_018389 SEQ ID NO 1780 NM_018410 SEQ ID NO 1783 NM_018439 SEQ ID NO 1785 NM_018454 SEQ ID NO 1786 NM_018455 SEQ ID NO 1787 NM_018465 SEQ ID NO 1788 NM_018471 SEQ ID NO 1789 NM_018478 SEQ ID NO 1790 NM_018479 SEQ ID NO 1791 NM_018529 SEQ ID NO 1793 NM_018556 SEQ ID NO 1794 NM_018569 SEQ ID NO 1795 NM_018584 SEQ ID NO 1796 NM_018653 SEQ ID NO 1797 NM_018660 SEQ ID NO 1798 NM_018683 SEQ ID NO 1799 NM_018685 SEQ ID NO 1800 NM_018686 SEQ ID NO 1801 NM_018695 SEQ ID NO 1802 NM_018728 SEQ ID NO 1803 NM_018840 SEQ ID NO 1804 NM_018842 SEQ ID NO 1805 NM_018950 SEQ ID NO 1806 NM_018988 SEQ ID NO 1807 NM_019000 SEQ ID NO 1808 NM_019013 SEQ ID NO 1809 NM_019025 SEQ ID NO 1810 NM_019027 SEQ ID NO 1811 NM_019041 SEQ ID NO 1812 NM_019044 SEQ ID NO 1813 NM_019063 SEQ ID NO 1815 NM_019084 SEQ ID NO 1816 NM_019554 SEQ ID NO 1817 NM_019845 SEQ ID NO 1818 NM_019858 SEQ ID NO 1819 NM_020130 SEQ ID NO 1820 NM_020133 SEQ ID NO 1821 NM_020143 SEQ ID NO 1822 NM_020150 SEQ ID NO 1823 NM_020163 SEQ ID NO 1824 NM_020166 SEQ ID NO 1825 NM_020169 SEQ ID NO 1826 NM_020179 SEQ ID NO 1827 NM_020184 SEQ ID NO 1828 NM_020186 SEQ ID NO 1829 NM_020188 SEQ ID NO 1830 NM_020189 SEQ ID NO 1831 NM_020197 SEQ ID NO 1832 NM_020199 SEQ ID NO 1833 NM_020215 SEQ ID NO 1834 NM_020347 SEQ ID NO 1836 NM_020365 SEQ ID NO 1837 NM_020386 SEQ ID NO 1838 NM_020445 SEQ ID NO 1839 NM_020639 SEQ ID NO 1840 NM_020659 SEQ ID NO 1841 NM_020675 SEQ ID NO 1842 NM_020686 SEQ ID NO 1843 NM_020974 SEQ ID NO 1844 NM_020978 SEQ ID NO 1845 NM_020979 SEQ ID NO 1846 NM_020980 SEQ ID NO 1847 NM_021000 SEQ ID NO 1849 NM_021004 SEQ ID NO 1850 NM_021025 SEQ ID NO 1851 NM_021063 SEQ ID NO 1852 NM_021065 SEQ ID NO 1853 NM_021077 SEQ ID NO 1854 NM_021095 SEQ ID NO 1855 NM_021101 SEQ ID NO 1856 NM_021103 SEQ ID NO 1857 NM_021128 SEQ ID NO 1858 NM_021147 SEQ ID NO 1859 NM_021151 SEQ ID NO 1860 NM_021181 SEQ ID NO 1861 NM_021190 SEQ ID NO 1862 NM_021198 SEQ ID NO 1863 NM_021200 SEQ ID NO 1864 NM_021203 SEQ ID NO 1865 NM_021238 SEQ ID NO 1866 NM_021242 SEQ ID NO 1867 S40706 SEQ ID NO 1869 S53354 SEQ ID NO 1870 S59184 SEQ ID NO 1871 S62138 SEQ ID NO 1872 U09848 SEQ ID NO 1873 U10991 SEQ ID NO 1874 U17077 SEQ ID NO 1875 U18919 SEQ ID NO 1876 U41387 SEQ ID NO 1877 U45975 SEQ ID NO 1878 U49835 SEQ ID NO 1879 U56725 SEQ ID NO 1880 U58033 SEQ ID NO 1881 U61167 SEQ ID NO 1882 U66042 SEQ ID NO 1883 U68385 SEQ ID NO 1885 U68494 SEQ ID NO 1886 U74612 SEQ ID NO 1887 U75968 SEQ ID NO 1888 U79293 SEQ ID NO 1889 U80736 SEQ ID NO 1890 U82987 SEQ ID NO 1891 U83115 SEQ ID NO 1892 U89715 SEQ ID NO 1893 U90916 SEQ ID NO 1894 U92544 SEQ ID NO 1895 U96131 SEQ ID NO 1896 U96394 SEQ ID NO 1897 W61000_RC SEQ ID NO 1898 X00437 SEQ ID NO 1899 X00497 SEQ ID NO 1900 X01394 SEQ ID NO 1901 X03084 SEQ ID NO 1902 X07834 SEQ ID NO 1905 X14356 SEQ ID NO 1906 X16302 SEQ ID NO 1907 X52486 SEQ ID NO 1909 X52882 SEQ ID NO 1910 X56807 SEQ ID NO 1911 X57809 SEQ ID NO 1912 X57819 SEQ ID NO 1913 X58529 SEQ ID NO 1914 X59405 SEQ ID NO 1915 X72475 SEQ ID NO 1918 X73617 SEQ ID NO 1919 X74794 SEQ ID NO 1920 X75315 SEQ ID NO 1921 X79782 SEQ ID NO 1922 X82693 SEQ ID NO 1923 X83301 SEQ ID NO 1924 X93006 SEQ ID NO 1926 X94232 SEQ ID NO 1927 X98834 SEQ ID NO 1929 X99142 SEQ ID NO 1930 Y14737 SEQ ID NO 1932 Z11887 SEQ ID NO 1933 Z48633 SEQ ID NO 1935 NM_004222 SEQ ID NO 1936 NM_016405 SEQ ID NO 1937 NM_017690 SEQ ID NO 1938 Contig29_RC SEQ ID NO 1939 Contig237_RC SEQ ID NO 1940 Contig263_RC SEQ ID NO 1941 Contig292_RC SEQ ID NO 1942 Contig382_RC SEQ ID NO 1944 Contig399_RC SEQ ID NO 1945 Contig448_RC SEQ ID NO 1946 Contig569_RC SEQ ID NO 1947 Contig580_RC SEQ ID NO 1948 Contig678_RC SEQ ID NO 1949 Contig706_RC SEQ ID NO 1950 Contig718_RC SEQ ID NO 1951 Contig719_RC SEQ ID NO 1952 Contig742_RC SEQ ID NO 1953 Contig753_RC SEQ ID NO 1954 Contig758_RC SEQ ID NO 1956 Contig760_RC SEQ ID NO 1957 Contig842_RC SEQ ID NO 1958 Contig848_RC SEQ ID NO 1959 Contig924_RC SEQ ID NO 1960 Contig974_RC SEQ ID NO 1961 Contig1018_RC SEQ ID NO 1962 Contig1056_RC SEQ ID NO 1963 Contig1061_RC SEQ ID NO 1964 Contig1129_RC SEQ ID NO 1965 Contig1148 SEQ ID NO 1966 Contig1239_RC SEQ ID NO 1967 Contig1277 SEQ ID NO 1968 Contig1333_RC SEQ ID NO 1969 Contig1386_RC SEQ ID NO 1970 Contig1389_RC SEQ ID NO 1971 Contig1418_RC SEQ ID NO 1972 Contig1462_RC SEQ ID NO 1973 Contig1505_RC SEQ ID NO 1974 Contig1540_RC SEQ ID NO 1975 Contig1584_RC SEQ ID NO 1976 Contig1632_RC SEQ ID NO 1977 Contig1682_RC SEQ ID NO 1978 Contig1778_RC SEQ ID NO 1979 Contig1829 SEQ ID NO 1981 Contig1838_RC SEQ ID NO 1982 Contig1938_RC SEQ ID NO 1983 Contig1970_RC SEQ ID NO 1984 Contig1998_RC SEQ ID NO 1985 Contig2099_RC SEQ ID NO 1986 Contig2143_RC SEQ ID NO 1987 Contig2237_RC SEQ ID NO 1988 Contig2429_RC SEQ ID NO 1990 Contig2504_RC SEQ ID NO 1991 Contig2512_RC SEQ ID NO 1992 Contig2575_RC SEQ ID NO 1993 Contig2578_RC SEQ ID NO 1994 Contig2639_RC SEQ ID NO 1995 Contig2647_RC SEQ ID NO 1996 Contig2657_RC SEQ ID NO 1997 Contig2728_RC SEQ ID NO 1998 Contig2745_RC SEQ ID NO 1999 Contig2811_RC SEQ ID NO 2000 Contig2873_RC SEQ ID NO 2001 Contig2883_RC SEQ ID NO 2002 Contig2915_RC SEQ ID NO 2003 Contig2928_RC SEQ ID NO 2004 Contig3024_RC SEQ ID NO 2005 Contig3094_RC SEQ ID NO 2006 Contig3164_RC SEQ ID NO 2007 Contig3495_RC SEQ ID NO 2009 Contig3607_RC SEQ ID NO 2010 Contig3659_RC SEQ ID NO 2011 Contig3677_RC SEQ ID NO 2012 Contig3682_RC SEQ ID NO 2013 Contig3734_RC SEQ ID NO 2014 Contig3834_RC SEQ ID NO 2015 Contig3876_RC SEQ ID NO 2016 Contig3902_RC SEQ ID NO 2017 Contig3940_RC SEQ ID NO 2018 Contig4380_RC SEQ ID NO 2019 Contig4388_RC SEQ ID NO 2020 Contig4467_RC SEQ ID NO 2021 Contig4949_RC SEQ ID NO 2023 Contig5348_RC SEQ ID NO 2024 Contig5403_RC SEQ ID NO 2025 Contig5716_RC SEQ ID NO 2026 Contig6118_RC SEQ ID NO 2027 Contig6164_RC SEQ ID NO 2028 Contig6181_RC SEQ ID NO 2029 Contig6514_RC SEQ ID NO 2030 Contig6612_RC SEQ ID NO 2031 Contig6881_RC SEQ ID NO 2032 Contig8165_RC SEQ ID NO 2033 Contig8221_RC SEQ ID NO 2034 Contig8347_RC SEQ ID NO 2035 Contig8364_RC SEQ ID NO 2036 Contig8888_RC SEQ ID NO 2038 Contig9259_RC SEQ ID NO 2039 Contig9541_RC SEQ ID NO 2040 Contig10268_RC SEQ ID NO 2041 Contig10363_RC SEQ ID NO 2042 Contig10437_RC SEQ ID NO 2043 Contig11086_RC SEQ ID NO 2045 Contig11275_RC SEQ ID NO 2046 Contig11648_RC SEQ ID NO 2047 Contig12216_RC SEQ ID NO 2048 Contig12369_RC SEQ ID NO 2049 Contig12814_RC SEQ ID NO 2050 Contig12951_RC SEQ ID NO 2051 Contig13480_RC SEQ ID NO 2052 Contig14284_RC SEQ ID NO 2053 Contig14390_RC SEQ ID NO 2054 Contig14780_RC SEQ ID NO 2055 Contig14954_RC SEQ ID NO 2056 Contig14981_RC SEQ ID NO 2057 Contig15692_RC SEQ ID NO 2058 Contig16192_RC SEQ ID NO 2059 Contig16759_RC SEQ ID NO 2061 Contig16786_RC SEQ ID NO 2062 Contig16905_RC SEQ ID NO 2063 Contig17103_RC SEQ ID NO 2064 Contig17105_RC SEQ ID NO 2065 Contig17248_RC SEQ ID NO 2066 Contig17345_RC SEQ ID NO 2067 Contig18502_RC SEQ ID NO 2069 Contig20156_RC SEQ ID NO 2071 Contig20302_RC SEQ ID NO 2073 Contig20600_RC SEQ ID NO 2074 Contig20617_RC SEQ ID NO 2075 Contig20629_RC SEQ ID NO 2076 Contig20651_RC SEQ ID NO 2077 Contig21130_RC SEQ ID NO 2078 Contig21185_RC SEQ ID NO 2079 Contig21421_RC SEQ ID NO 2080 Contig21787_RC SEQ ID NO 2081 Contig21812_RC SEQ ID NO 2082 Contig22418_RC SEQ ID NO 2083 Contig23085_RC SEQ ID NO 2084 Contig23454_RC SEQ ID NO 2085 Contig24138_RC SEQ ID NO 2086 Contig24252_RC SEQ ID NO 2087 Contig24655_RC SEQ ID NO 2089 Contig25055_RC SEQ ID NO 2090 Contig25290_RC SEQ ID NO 2091 Contig25343_RC SEQ ID NO 2092 Contig25362_RC SEQ ID NO 2093 Contig25617_RC SEQ ID NO 2094 Contig25659_RC SEQ ID NO 2095 Contig25722_RC SEQ ID NO 2096 Contig25809_RC SEQ ID NO 2097 Contig25991 SEQ ID NO 2098 Contig26022_RC SEQ ID NO 2099 Contig26077_RC SEQ ID NO 2100 Contig26310_RC SEQ ID NO 2101 Contig26371_RC SEQ ID NO 2102 Contig26438_RC SEQ ID NO 2103 Contig26706_RC SEQ ID NO 2104 Contig27088_RC SEQ ID NO 2105 Contig27186_RC SEQ ID NO 2106 Contig27228_RC SEQ ID NO 2107 Contig27344_RC SEQ ID NO 2109 Contig27386_RC SEQ ID NO 2110 Contig27624_RC SEQ ID NO 2111 Contig27749_RC SEQ ID NO 2112 Contig27882_RC SEQ ID NO 2113 Contig27915_RC SEQ ID NO 2114 Contig28030_RC SEQ ID NO 2115 Contig28081_RC SEQ ID NO 2116 Contig28152_RC SEQ ID NO 2117 Contig28550_RC SEQ ID NO 2119 Contig28552_RC SEQ ID NO 2120 Contig28712_RC SEQ ID NO 2121 Contig28888_RC SEQ ID NO 2122 Contig28947_RC SEQ ID NO 2123 Contig29126_RC SEQ ID NO 2124 Contig29193_RC SEQ ID NO 2125 Contig29369_RC SEQ ID NO 2126 Contig29639_RC SEQ ID NO 2127 Contig30047_RC SEQ ID NO 2129 Contig30154_RC SEQ ID NO 2131 Contig30209_RC SEQ ID NO 2132 Contig30213_RC SEQ ID NO 2133 Contig30230_RC SEQ ID NO 2134 Contig30267_RC SEQ ID NO 2135 Contig30390_RC SEQ ID NO 2136 Contig30480_RC SEQ ID NO 2137 Contig30609_RC SEQ ID NO 2138 Contig30934_RC SEQ ID NO 2139 Contig31150_RC SEQ ID NO 2140 Contig31186_RC SEQ ID NO 2141 Contig31251_RC SEQ ID NO 2142 Contig31288_RC SEQ ID NO 2143 Contig31291_RC SEQ ID NO 2144 Contig31295_RC SEQ ID NO 2145 Contig31424_RC SEQ ID NO 2146 Contig31449_RC SEQ ID NO 2147 Contig31596_RC SEQ ID NO 2148 Contig31864_RC SEQ ID NO 2149 Contig31928_RC SEQ ID NO 2150 Contig31966_RC SEQ ID NO 2151 Contig31986_RC SEQ ID NO 2152 Contig32084_RC SEQ ID NO 2153 Contig32105_RC SEQ ID NO 2154 Contig32185_RC SEQ ID NO 2156 Contig32242_RC SEQ ID NO 2157 Contig32322_RC SEQ ID NO 2158 Contig32336_RC SEQ ID NO 2159 Contig32558_RC SEQ ID NO 2160 Contig32798_RC SEQ ID NO 2161 Contig33005_RC SEQ ID NO 2162 Contig33230_RC SEQ ID NO 2163 Contig33260_RC SEQ ID NO 2164 Contig33654_RC SEQ ID NO 2166 Contig33741_RC SEQ ID NO 2167 Contig33771_RC SEQ ID NO 2168 Contig33814_RC SEQ ID NO 2169 Contig33815_RC SEQ ID NO 2170 Contig33833 SEQ ID NO 2171 Contig33998_RC SEQ ID NO 2172 Contig34079 SEQ ID NO 2173 Contig34080_RC SEQ ID NO 2174 Contig34222_RC SEQ ID NO 2175 Contig34233_RC SEQ ID NO 2176 Contig34303_RC SEQ ID NO 2177 Contig34393_RC SEQ ID NO 2178 Contig34477_RC SEQ ID NO 2179 Contig34766_RC SEQ ID NO 2181 Contig34952 SEQ ID NO 2182 Contig34989_RC SEQ ID NO 2183 Contig35030_RC SEQ ID NO 2184 Contig35251_RC SEQ ID NO 2185 Contig35629_RC SEQ ID NO 2186 Contig35635_RC SEQ ID NO 2187 Contig35763_RC SEQ ID NO 2188 Contig35814_RC SEQ ID NO 2189 Contig35896_RC SEQ ID NO 2190 Contig35976_RC SEQ ID NO 2191 Contig36042_RC SEQ ID NO 2192 Contig36081_RC SEQ ID NO 2193 Contig36152_RC SEQ ID NO 2194 Contig36193_RC SEQ ID NO 2195 Contig36312_RC SEQ ID NO 2196 Contig36323_RC SEQ ID NO 2197 Contig36339_RC SEQ ID NO 2198 Contig36647_RC SEQ ID NO 2199 Contig36744_RC SEQ ID NO 2200 Contig36761_RC SEQ ID NO 2201 Contig36879_RC SEQ ID NO 2202 Contig36900_RC SEQ ID NO 2203 Contig37015_RC SEQ ID NO 2204 Contig37024_RC SEQ ID NO 2205 Contig37072_RC SEQ ID NO 2207 Contig37140_RC SEQ ID NO 2208 Contig37141_RC SEQ ID NO 2209 Contig37204_RC SEQ ID NO 2210 Contig37281_RC SEQ ID NO 2211 Contig37287_RC SEQ ID NO 2212 Contig37439_RC SEQ ID NO 2213 Contig37562_RC SEQ ID NO 2214 Contig37571_RC SEQ ID NO 2215 Contig37598 SEQ ID NO 2216 Contig37758_RC SEQ ID NO 2217 Contig37778_RC SEQ ID NO 2218 Contig37884_RC SEQ ID NO 2219 Contig37946_RC SEQ ID NO 2220 Contig38170_RC SEQ ID NO 2221 Contig38288_RC SEQ ID NO 2223 Contig38398_RC SEQ ID NO 2224 Contig38580_RC SEQ ID NO 2226 Contig38630_RC SEQ ID NO 2227 Contig38652_RC SEQ ID NO 2228 Contig38683_RC SEQ ID NO 2229 Contig38726_RC SEQ ID NO 2230 Contig38791_RC SEQ ID NO 2231 Contig38901_RC SEQ ID NO 2232 Contig38983_RC SEQ ID NO 2233 Contig39090_RC SEQ ID NO 2234 Contig39132_RC SEQ ID NO 2235 Contig39157_RC SEQ ID NO 2236 Contig39226_RC SEQ ID NO 2237 Contig39285_RC SEQ ID NO 2238 Contig39556_RC SEQ ID NO 2239 Contig39591_RC SEQ ID NO 2240 Contig39826_RC SEQ ID NO 2241 Contig39845_RC SEQ ID NO 2242 Contig39891_RC SEQ ID NO 2243 Contig39922_RC SEQ ID NO 2244 Contig39960_RC SEQ ID NO 2245 Contig40026_RC SEQ ID NO 2246 Contig40121_RC SEQ ID NO 2247 Contig40128_RC SEQ ID NO 2248 Contig40146 SEQ ID NO 2249 Contig40208_RC SEQ ID NO 2250 Contig40212_RC SEQ ID NO 2251 Contig40238_RC SEQ ID NO 2252 Contig40434_RC SEQ ID NO 2253 Contig40446_RC SEQ ID NO 2254 Contig40500_RC SEQ ID NO 2255 Contig40573_RC SEQ ID NO 2256 Contig40813_RC SEQ ID NO 2258 Contig40816_RC SEQ ID NO 2259 Contig40845_RC SEQ ID NO 2261 Contig40889_RC SEQ ID NO 2262 Contig41035 SEQ ID NO 2263 Contig41234_RC SEQ ID NO 2264 Contig41413_RC SEQ ID NO 2266 Contig41521_RC SEQ ID NO 2267 Contig41530_RC SEQ ID NO 2268 Contig41590 SEQ ID NO 2269 Contig41618_RC SEQ ID NO 2270 Contig41624_RC SEQ ID NO 2271 Contig41635_RC SEQ ID NO 2272 Contig41676_RC SEQ ID NO 2273 Contig41689_RC SEQ ID NO 2274 Contig41804_RC SEQ ID NO 2275 Contig41887_RC SEQ ID NO 2276 Contig41905_RC SEQ ID NO 2277 Contig41954_RC SEQ ID NO 2278 Contig41983_RC SEQ ID NO 2279 Contig42006_RC SEQ ID NO 2280 Contig42014_RC SEQ ID NO 2281 Contig42036_RC SEQ ID NO 2282 Contig42041_RC SEQ ID NO 2283 Contig42139 SEQ ID NO 2284 Contig42161_RC SEQ ID NO 2285 Contig42220_RC SEQ ID NO 2286 Contig42306_RC SEQ ID NO 2287 Contig42311_RC SEQ ID NO 2288 Contig42313_RC SEQ ID NO 2289 Contig42402_RC SEQ ID NO 2290 Contig42421_RC SEQ ID NO 2291 Contig42430_RC SEQ ID NO 2292 Contig42431_RC SEQ ID NO 2293 Contig42542_RC SEQ ID NO 2294 Contig42582 SEQ ID NO 2295 Contig42631_RC SEQ ID NO 2296 Contig42751_RC SEQ ID NO 2297 Contig42759_RC SEQ ID NO 2298 Contig43054 SEQ ID NO 2299 Contig43079_RC SEQ ID NO 2300 Contig43195_RC SEQ ID NO 2301 Contig43368_RC SEQ ID NO 2302 Contig43410_RC SEQ ID NO 2303 Contig43476_RC SEQ ID NO 2304 Contig43549_RC SEQ ID NO 2305 Contig43645_RC SEQ ID NO 2306 Contig43648_RC SEQ ID NO 2307 Contig43673_RC SEQ ID NO 2308 Contig43679_RC SEQ ID NO 2309 Contig43694_RC SEQ ID NO 2310 Contig43747_RC SEQ ID NO 2311 Contig43918_RC SEQ ID NO 2312 Contig43983_RC SEQ ID NO 2313 Contig44040_RC SEQ ID NO 2314 Contig44064_RC SEQ ID NO 2315 Contig44195_RC SEQ ID NO 2316 Contig44226_RC SEQ ID NO 2317 Contig44289_RC SEQ ID NO 2320 Contig44310_RC SEQ ID NO 2321 Contig44409 SEQ ID NO 2322 Contig44413_RC SEQ ID NO 2323 Contig44451_RC SEQ ID NO 2324 Contig44585_RC SEQ ID NO 2325 Contig44656_RC SEQ ID NO 2326 Contig44703_RC SEQ ID NO 2327 Contig44708_RC SEQ ID NO 2328 Contig44757_RC SEQ ID NO 2329 Contig44829_RC SEQ ID NO 2331 Contig44870 SEQ ID NO 2332 Contig44893_RC SEQ ID NO 2333 Contig44909_RC SEQ ID NO 2334 Contig44939_RC SEQ ID NO 2335 Contig45022_RC SEQ ID NO 2336 Contig45032_RC SEQ ID NO 2337 Contig45041_RC SEQ ID NO 2338 Contig45049_RC SEQ ID NO 2339 Contig45090_RC SEQ ID NO 2340 Contig45156_RC SEQ ID NO 2341 Contig45316_RC SEQ ID NO 2342 Contig45321 SEQ ID NO 2343 Contig45375_RC SEQ ID NO 2345 Contig45443_RC SEQ ID NO 2346 Contig45454_RC SEQ ID NO 2347 Contig45537_RC SEQ ID NO 2348 Contig45588_RC SEQ ID NO 2349 Contig45708_RC SEQ ID NO 2350 Contig45816_RC SEQ ID NO 2351 Contig45847_RC SEQ ID NO 2352 Contig45891_RC SEQ ID NO 2353 Contig46056_RC SEQ ID NO 2354 Contig46062_RC SEQ ID NO 2355 Contig46075_RC SEQ ID NO 2356 Contig46164_RC SEQ ID NO 2357 Contig46218_RC SEQ ID NO 2358 Contig46223_RC SEQ ID NO 2359 Contig46244_RC SEQ ID NO 2360 Contig46262_RC SEQ ID NO 2361 Contig46362_RC SEQ ID NO 2364 Contig46443_RC SEQ ID NO 2365 Contig46553_RC SEQ ID NO 2367 Contig46597_RC SEQ ID NO 2368 Contig46653_RC SEQ ID NO 2369 Contig46709_RC SEQ ID NO 2370 Contig46777_RC SEQ ID NO 2371 Contig46802_RC SEQ ID NO 2372 Contig46890_RC SEQ ID NO 2374 Contig46922_RC SEQ ID NO 2375 Contig46934_RC SEQ ID NO 2376 Contig46937_RC SEQ ID NO 2377 Contig46991_RC SEQ ID NO 2378 Contig47016_RC SEQ ID NO 2379 Contig47045_RC SEQ ID NO 2380 Contig47106_RC SEQ ID NO 2381 Contig47146_RC SEQ ID NO 2382 Contig47230_RC SEQ ID NO 2383 Contig47405_RC SEQ ID NO 2384 Contig47456_RC SEQ ID NO 2385 Contig47465_RC SEQ ID NO 2386 Contig47498_RC SEQ ID NO 2387 Contig47578_RC SEQ ID NO 2388 Contig47645_RC SEQ ID NO 2389 Contig47680_RC SEQ ID NO 2390 Contig47781_RC SEQ ID NO 2391 Contig47814_RC SEQ ID NO 2392 Contig48004_RC SEQ ID NO 2393 Contig48043_RC SEQ ID NO 2394 Contig48057_RC SEQ ID NO 2395 Contig48076_RC SEQ ID NO 2396 Contig48249_RC SEQ ID NO 2397 Contig48263_RC SEQ ID NO 2398 Contig48270_RC SEQ ID NO 2399 Contig48328_RC SEQ ID NO 2400 Contig48518_RC SEQ ID NO 2401 Contig48572_RC SEQ ID NO 2402 Contig48659_RC SEQ ID NO 2403 Contig48722_RC SEQ ID NO 2404 Contig48774_RC SEQ ID NO 2405 Contig48776_RC SEQ ID NO 2406 Contig48800_RC SEQ ID NO 2407 Contig48806_RC SEQ ID NO 2408 Contig48852_RC SEQ ID NO 2409 Contig48900_RC SEQ ID NO 2410 Contig48913_RC SEQ ID NO 2411 Contig48970_RC SEQ ID NO 2413 Contig49058_RC SEQ ID NO 2414 Contig49063_RC SEQ ID NO 2415 Contig49093 SEQ ID NO 2416 Contig49098_RC SEQ ID NO 2417 Contig49169_RC SEQ ID NO 2418 Contig49233_RC SEQ ID NO 2419 Contig49270_RC SEQ ID NO 2420 Contig49282_RC SEQ ID NO 2421 Contig49289_RC SEQ ID NO 2422 Contig49342_RC SEQ ID NO 2423 Contig49344 SEQ ID NO 2424 Contig49388_RC SEQ ID NO 2425 Contig49405_RC SEQ ID NO 2426 Contig49445_RC SEQ ID NO 2427 Contig49468_RC SEQ ID NO 2428 Contig49509_RC SEQ ID NO 2429 Contig49578_RC SEQ ID NO 2431 Contig49581_RC SEQ ID NO 2432 Contig49631_RC SEQ ID NO 2433 Contig49673_RC SEQ ID NO 2435 Contig49743_RC SEQ ID NO 2436 Contig49790_RC SEQ ID NO 2437 Contig49818_RC SEQ ID NO 2438 Contig49849_RC SEQ ID NO 2439 Contig49855 SEQ ID NO 2440 Contig49910_RC SEQ ID NO 2441 Contig49948_RC SEQ ID NO 2442 Contig50004_RC SEQ ID NO 2443 Contig50094 SEQ ID NO 2444 Contig50120_RC SEQ ID NO 2446 Contig50153_RC SEQ ID NO 2447 Contig50189_RC SEQ ID NO 2448 Contig50276_RC SEQ ID NO 2449 Contig50288_RC SEQ ID NO 2450 Contig50297_RC SEQ ID NO 2451 Contig50391_RC SEQ ID NO 2452 Contig50410 SEQ ID NO 2453 Contig50523_RC SEQ ID NO 2454 Contig50529 SEQ ID NO 2455 Contig50588_RC SEQ ID NO 2456 Contig50592 SEQ ID NO 2457 Contig50669_RC SEQ ID NO 2458 Contig50719_RC SEQ ID NO 2460 Contig50728_RC SEQ ID NO 2461 Contig50731_RC SEQ ID NO 2462 Contig50802_RC SEQ ID NO 2463 Contig50822_RC SEQ ID NO 2464 Contig50850_RC SEQ ID NO 2466 Contig50860_RC SEQ ID NO 2467 Contig50913_RC SEQ ID NO 2468 Contig50950_RC SEQ ID NO 2469 Contig51066_RC SEQ ID NO 2470 Contig51105_RC SEQ ID NO 2472 Contig51117_RC SEQ ID NO 2473 Contig51196_RC SEQ ID NO 2474 Contig51235_RC SEQ ID NO 2475 Contig51254_RC SEQ ID NO 2476 Contig51352_RC SEQ ID NO 2477 Contig51369_RC SEQ ID NO 2478 Contig51392_RC SEQ ID NO 2479 Contig51403_RC SEQ ID NO 2480 Contig51685_RC SEQ ID NO 2483 Contig51726_RC SEQ ID NO 2484 Contig51742_RC SEQ ID NO 2485 Contig51749_RC SEQ ID NO 2486 Contig51775_RC SEQ ID NO 2487 Contig51800 SEQ ID NO 2488 Contig51809_RC SEQ ID NO 2489 Contig51821_RC SEQ ID NO 2490 Contig51888_RC SEQ ID NO 2491 Contig51953_RC SEQ ID NO 2493 Contig51967_RC SEQ ID NO 2495 Contig51981_RC SEQ ID NO 2496 Contig51994_RC SEQ ID NO 2497 Contig52082_RC SEQ ID NO 2498 Contig52094_RC SEQ ID NO 2499 Contig52320 SEQ ID NO 2500 Contig52398_RC SEQ ID NO 2501 Contig52425_RC SEQ ID NO 2503 Contig52482_RC SEQ ID NO 2504 Contig52543_RC SEQ ID NO 2505 Contig52553_RC SEQ ID NO 2506 Contig52579_RC SEQ ID NO 2507 Contig52603_RC SEQ ID NO 2508 Contig52639_RC SEQ ID NO 2509 Contig52641_RC SEQ ID NO 2510 Contig52684 SEQ ID NO 2511 Contig52705_RC SEQ ID NO 2512 Contig52720_RC SEQ ID NO 2513 Contig52722_RC SEQ ID NO 2514 Contig52723_RC SEQ ID NO 2515 Contig52740_RC SEQ ID NO 2516 Contig52779_RC SEQ ID NO 2517 Contig52957_RC SEQ ID NO 2518 Contig52994_RC SEQ ID NO 2519 Contig53022_RC SEQ ID NO 2520 Contig53038_RC SEQ ID NO 2521 Contig53047_RC SEQ ID NO 2522 Contig53130 SEQ ID NO 2523 Contig53183_RC SEQ ID NO 2524 Contig53242_RC SEQ ID NO 2526 Contig53248_RC SEQ ID NO 2527 Contig53260_RC SEQ ID NO 2528 Contig53296_RC SEQ ID NO 2531 Contig53307_RC SEQ ID NO 2532 Contig53314_RC SEQ ID NO 2533 Contig53401_RC SEQ ID NO 2534 Contig53550_RC SEQ ID NO 2535 Contig53551_RC SEQ ID NO 2536 Contig53598_RC SEQ ID NO 2537 Contig53646_RC SEQ ID NO 2538 Contig53658_RC SEQ ID NO 2539 Contig53698_RC SEQ ID NO 2540 Contig53719_RC SEQ ID NO 2541 Contig53742_RC SEQ ID NO 2542 Contig53757_RC SEQ ID NO 2543 Contig53870_RC SEQ ID NO 2544 Contig53952_RC SEQ ID NO 2546 Contig53962_RC SEQ ID NO 2547 Contig53968_RC SEQ ID NO 2548 Contig54113_RC SEQ ID NO 2549 Contig54142_RC SEQ ID NO 2550 Contig54232_RC SEQ ID NO 2551 Contig54242_RC SEQ ID NO 2552 Contig54260_RC SEQ ID NO 2553 Contig54263_RC SEQ ID NO 2554 Contig54295_RC SEQ ID NO 2555 Contig54318_RC SEQ ID NO 2556 Contig54325_RC SEQ ID NO 2557 Contig54389_RC SEQ ID NO 2558 Contig54394_RC SEQ ID NO 2559 Contig54414_RC SEQ ID NO 2560 Contig54425 SEQ ID NO 2561 Contig54477_RC SEQ ID NO 2562 Contig54503_RC SEQ ID NO 2563 Contig54534_RC SEQ ID NO 2564 Contig54560_RC SEQ ID NO 2566 Contig54581_RC SEQ ID NO 2567 Contig54609_RC SEQ ID NO 2568 Contig54666_RC SEQ ID NO 2569 Contig54667_RC SEQ ID NO 2570 Contig54726_RC SEQ ID NO 2571 Contig54742_RC SEQ ID NO 2572 Contig54745_RC SEQ ID NO 2573 Contig54757_RC SEQ ID NO 2574 Contig54761_RC SEQ ID NO 2575 Contig54813_RC SEQ ID NO 2576 Contig54867_RC SEQ ID NO 2577 Contig54895_RC SEQ ID NO 2578 Contig54898_RC SEQ ID NO 2579 Contig54913_RC SEQ ID NO 2580 Contig54965_RC SEQ ID NO 2582 Contig54968_RC SEQ ID NO 2583 Contig55069_RC SEQ ID NO 2584 Contig55181_RC SEQ ID NO 2585 Contig55188_RC SEQ ID NO 2586 Contig55221_RC SEQ ID NO 2587 Contig55254_RC SEQ ID NO 2588 Contig55265_RC SEQ ID NO 2589 Contig55377_RC SEQ ID NO 2591 Contig55397_RC SEQ ID NO 2592 Contig55448_RC SEQ ID NO 2593 Contig55468_RC SEQ ID NO 2594 Contig55500_RC SEQ ID NO 2595 Contig55538_RC SEQ ID NO 2596 Contig55558_RC SEQ ID NO 2597 Contig55606_RC SEQ ID NO 2598 Contig55674_RC SEQ ID NO 2599 Contig55725_RC SEQ ID NO 2600 Contig55728_RC SEQ ID NO 2601 Contig55756_RC SEQ ID NO 2602 Contig55769_RC SEQ ID NO 2603 Contig55771_RC SEQ ID NO 2605 Contig55813_RC SEQ ID NO 2607 Contig55829_RC SEQ ID NO 2608 Contig55852_RC SEQ ID NO 2609 Contig55883_RC SEQ ID NO 2610 Contig55920_RC SEQ ID NO 2611 Contig55940_RC SEQ ID NO 2612 Contig55950_RC SEQ ID NO 2613 Contig55991_RC SEQ ID NO 2614 Contig55997_RC SEQ ID NO 2615 Contig56023_RC SEQ ID NO 2616 Contig56030_RC SEQ ID NO 2617 Contig56093_RC SEQ ID NO 2618 Contig56205_RC SEQ ID NO 2621 Contig56270_RC SEQ ID NO 2622 Contig56276_RC SEQ ID NO 2623 Contig56291_RC SEQ ID NO 2624 Contig56298_RC SEQ ID NO 2625 Contig56307 SEQ ID NO 2627 Contig56390_RC SEQ ID NO 2628 Contig56434_RC SEQ ID NO 2629 Contig56457_RC SEQ ID NO 2630 Contig56534_RC SEQ ID NO 2631 Contig56670_RC SEQ ID NO 2632 Contig56678_RC SEQ ID NO 2633 Contig56742_RC SEQ ID NO 2634 Contig56759_RC SEQ ID NO 2635 Contig56765_RC SEQ ID NO 2636 Contig56843_RC SEQ ID NO 2637 Contig57011_RC SEQ ID NO 2638 Contig57023_RC SEQ ID NO 2639 Contig57057_RC SEQ ID NO 2640 Contig57076_RC SEQ ID NO 2641 Contig57081_RC SEQ ID NO 2642 Contig57091_RC SEQ ID NO 2643 Contig57138_RC SEQ ID NO 2644 Contig57173_RC SEQ ID NO 2645 Contig57230_RC SEQ ID NO 2646 Contig57258_RC SEQ ID NO 2647 Contig57270_RC SEQ ID NO 2648 Contig57272_RC SEQ ID NO 2649 Contig57344_RC SEQ ID NO 2650 Contig57430_RC SEQ ID NO 2651 Contig57458_RC SEQ ID NO 2652 Contig57493_RC SEQ ID NO 2653 Contig57584_RC SEQ ID NO 2654 Contig57595 SEQ ID NO 2655 Contig57602_RC SEQ ID NO 2656 Contig57609_RC SEQ ID NO 2657 Contig57610_RC SEQ ID NO 2658 Contig57644_RC SEQ ID NO 2659 Contig57725_RC SEQ ID NO 2660 Contig57739_RC SEQ ID NO 2661 Contig57825_RC SEQ ID NO 2662 Contig57864_RC SEQ ID NO 2663 Contig57940_RC SEQ ID NO 2664 Contig58260_RC SEQ ID NO 2665 Contig58272_RC SEQ ID NO 2666 Contig58301_RC SEQ ID NO 2667 Contig58368_RC SEQ ID NO 2668 Contig58471_RC SEQ ID NO 2669 Contig58755_RC SEQ ID NO 2671 Contig59120_RC SEQ ID NO 2672 Contig60157_RC SEQ ID NO 2673 Contig60864_RC SEQ ID NO 2676 Contig61254_RC SEQ ID NO 2677 Contig61815 SEQ ID NO 2678 Contig61975 SEQ ID NO 2679 Contig62306 SEQ ID NO 2680 Contig62568_RC SEQ ID NO 2681 Contig62922_RC SEQ ID NO 2682 Contig62964_RC SEQ ID NO 2683 Contig63520_RC SEQ ID NO 2685 Contig63649_RC SEQ ID NO 2686 Contig63683_RC SEQ ID NO 2687 Contig63748_RC SEQ ID NO 2688 Contig64502 SEQ ID NO 2689 Contig64688 SEQ ID NO 2690 Contig64775_RC SEQ ID NO 2691 Contig65227 SEQ ID NO 2692 Contig65663 SEQ ID NO 2693 Contig65785_RC SEQ ID NO 2694 Contig65900 SEQ ID NO 2695 Contig66219_RC SEQ ID NO 2696 Contig66705_RC SEQ ID NO 2697 Contig66759_RC SEQ ID NO 2698 Contig67182_RC SEQ ID NO 2699

[0087] TABLE 2 550 preferred ER status markers drawn from Table 1. Identifier Correlation Name Description NM_002051 0.763977 GATA3 GATA-binding protein 3 AB020689 0.753592 KIAA0882 KIAA0882 protein NM_001218 0.753225 CA12 carbonic anhydrase XII NM_000125 0.748421 ESR1 estrogen receptor 1 Contig56678_RC 0.747816 ESTs NM_004496 0.729116 HNF3A hepatocyte nuclear factor 3, alpha NM_017732 0.713398 FLJ20262 hypothetical protein FLJ20262 NM_006806 −0.712678 BTG3 BTG family, member 3 Contig56390_RC 0.705940 ESTs Contig37571_RC 0.704468 ESTs NM_004559 −0.701617 NSEP1 nuclease sensitive element binding protein 1 Contig50153_RC −0.696652 ESTs, Weakly similar to LKHU proteoglycan link protein precursor [H. sapiens] NM_012155 0.694332 EMAP-2 microtubule-associated protein like echinoderm EMAP Contig237_RC 0.687485 FLJ21127 hypothetical protein FLJ21127 NM_019063 −0.686064 C2ORF2 chromosome 2 open reading frame 2 NM_012219 −0.680900 MRAS muscle RAS oncogene homolog NM_001982 0.676114 ERBB3 v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 3 NM_006623 −0.675090 PHGDH phosphoglycerate dehydrogenase NM_000636 −0.674282 SOD2 superoxide dismutase 2, mitochondrial NM_006017 −0.670353 PROML1 prominin (mouse)-like 1 Contig57940_RC 0.667915 MAP-1 MAP-1 protein Contig46934_RC 0.666908 ESTs, Weakly similar to JE0350 Anterior gradient-2 [H. sapiens] NM_005080 0.665772 XBP1 X-box binding protein 1 NM_014246 0.665725 CELSR1 cadherin, EGF LAG seven-pass G- type receptor 1, flamingo (Drosophila) homolog Contig54667_RC −0.663727 Human DNA sequence from clone RP1-187J11 on chromosome 6q11.1-22.33. Contains the gene for a novel protein similar to S. pombe and S. cerevisiae predicted proteins, the gene for a novel protein similar to protein kinase C inhibitors, the 3′ end of the gene for a novel protein similar to Drosophila L82 and predicted worm proteins, ESTs, STSs, GSSs and two putative CpG islands Contig51994_RC 0.663715 ESTs, Weakly similar to B0416.1 [C. elegans] NM_016337 0.663006 RNB6 RNB6 NM_015640 −0.660165 PAI-RBP1 PAI-1 mRNA-binding protein X07834 −0.657798 SOD2 superoxide dismutase 2, mitochondrial NM_012319 0.657666 LIV-1 LIV-1 protein, estrogen regulated Contig41887_RC 0.656042 ESTs, Weakly similar to Homolog of rat Zymogen granule membrane protein [H. sapiens] NM_003462 0.655349 P28 dynein, axonemal, light intermediate polypeptide Contig58301_RC 0.654268 Homo sapiens mRNA; cDNA DKFZp667D095 (from clone DKFZp667D095) NM_005375 0.653783 MYB v-myb avian myeloblastosis viral oncogene homolog NM_017447 −0.652445 YG81 hypothetical protein LOC54149 Contig924_RC −0.650658 ESTs M55914 −0.650181 MPB1 MYC promoter-binding protein 1 NM_006004 −0.649819 UQCRH ubiquinol-cytochrome c reductase hinge protein NM_000964 0.649072 RARA retinoic acid receptor, alpha NM_013301 0.647583 HSU79303 protein predicted by clone 23882 AB023211 −0.647403 PDI2 peptidyl arginine deiminase, type II NM_016629 −0.646412 LOC51323 hypothetical protein K02403 0.645532 C4A complement component 4A NM_016405 −0.642201 HSU93243 Ubc6p homolog Contig46597_RC 0.641733 ESTs Contig55377_RC 0.640310 ESTs NM_001207 0.637800 BTF3 basic transcription factor 3 NM_018166 0.636422 FLJ10647 hypothetical protein FLJ10647 AL110202 −0.635398 Homo sapiens mRNA; cDNA DKFZp586I2022 (from clone DKFZp586I2022) AL133105 −0.635201 DKFZp434F2322 hypothetical protein DKFZp434F2322 NM_016839 −0.635169 RBMS1 RNA binding motif, single stranded interacting protein 1 Contig53130 −0.634812 ESTs, Weakly similar to hyperpolarization-activated cyclic nucleotide-gated channel hHCN2 [H. sapiens] NM_018014 −0.634460 BCL11A B-cell CLL/lymphoma 11A (zinc finger protein) NM_006769 −0.632197 LMO4 LIM domain only 4 U92544 0.631170 JCL-1 hepatocellular carcinoma associated protein; breast cancer associated gene 1 Contig49233_RC −0.631047 Homo sapiens, Similar to nuclear receptor binding factor 2, clone IMAGE: 3463191, mRNA, partial cds AL133033 0.629690 KIAA1025 KIAA1025 protein AL049265 0.629414 Homo sapiens mRNA; cDNA DKFZp564F053 (from clone DKFZp564F053) NM_018728 0.627989 MYO5C myosin 5C NM_004780 0.627856 TCEAL1 transcription elongation factor A (SII)-like 1 Contig760_RC 0.627132 ESTs Contig399_RC 0.626543 FLJ12538 hypothetical protein FLJ12538 similar to ras-related protein RAB17 M83822 0.625092 CDC4L cell division cycle 4-like NM_001255 −0.625089 CDC20 CDC20 (cell division cycle 20, S. cerevisiae, homolog) NM_006739 −0.624903 MCM5 minichromosome maintenance deficient (S. cerevisiae) 5 (cell division cycle 46) NM_002888 −0.624664 RARRES1 retinoic acid receptor responder (tazarotene induced) 1 NM_003197 0.623850 TCEB1L transcription elongation factor B (SIII), polypeptide 1-like NM_006787 0.623625 JCL-1 hepatocellular carcinoma associated protein; breast cancer associated gene 1 Contig49342_RC 0.622179 ESTs AL133619 0.621719 Homo sapiens mRNA; cDNA DKFZp434E2321 (from clone DKFZp434E2321); partial cds AL133622 0.621577 KIAA0876 KIAA0876 protein NM_004648 −0.621532 PTPNS1 protein tyrosine phosphatase, non- receptor type substrate 1 NM_001793 −0.621530 CDH3 cadherin 3, type 1, P-cadherin (placental) NM_003217 0.620915 TEGT testis enhanced gene transcript (BAX inhibitor 1) NM_001551 0.620832 IGBP1 immunoglobulin (CD79A) binding protein 1 NM_002539 −0.620683 ODC1 ornithine decarboxylase 1 Contig55997_RC −0.619932 ESTs NM_000633 0.619547 BCL2 B-cell CLL/lymphoma 2 NM_016267 −0.619096 TONDU TONDU Contig3659_RC 0.618048 FLJ21174 hypothetical protein FLJ21174 NM_000191 0.617250 HMGCL 3-hydroxymethyl-3-methylglutaryl- Coenzyme A lyase (hydroxymethylglutaricaciduria) NM_001267 0.616890 CHAD chondroadherin Contig39090_RC 0.616385 ESTs AF055270 −0.616268 HSSG1 heat-shock suppressed protein 1 Contig43054 0.616015 FLJ21603 hypothetical protein FLJ21603 NM_001428 −0.615855 ENO1 enolase 1, (alpha) Contig51369_RC 0.615466 ESTs Contig36647_RC 0.615310 GFRA1 GDNF family receptor alpha 1 NM_014096 −0.614832 PRO1659 PRO1659 protein NM_015937 0.614735 LOC51604 CGI-06 protein Contig49790_RC −0.614463 ESTs NM_006759 −0.614279 UGP2 UDP-glucose pyrophosphorylase 2 Contig53598_RC −0.613787 FLJ11413 hypothetical protein FLJ11413 AF113132 −0.613561 PSA phosphoserine aminotransferase AK000004 0.613001 Homo sapiens mRNA for FLJ00004 protein, partial cds Contig52543_RC 0.612960 Homo sapiens cDNA FLJ13945 fis, clone Y79AA1000969 AB032966 −0.611917 KIAA1140 KIAA1140 protein AL080192 0.611544 Homo sapiens cDNA: FLJ21238 fis, clone COL01115 X56807 −0.610654 DSC2 desmocollin 2 Contig30390_RC 0.609614 ESTs AL137362 0.609121 FLJ22237 hypothetical protein FLJ22237 NM_014211 −0.608585 GABRP gamma-aminobutyric acid (GABA) A receptor, pi NM_006696 0.608474 SMAP thyroid hormone receptor coactivating protein Contig45588_RC −0.608273 Homo sapiens cDNA: FLJ22610 fis, clone HSI04930 NM_003358 0.608244 UGCG UDP-glucose ceramide glucosyltransferase NM_006153 −0.608129 NCK1 NCK adaptor protein 1 NM_001453 −0.606939 FOXC1 forkhead box C1 Contig54666_RC 0.606475 oy65e02.x1 NCI_CGAP_CLL1 Homo sapiens cDNA clone IMAGE: 1670714 3′ similar to TR: Q29168 Q29168 UNKNOWN PROTEIN;, mRNA sequence. NM_005945 −0.605945 MPB1 MYC promoter-binding protein 1 Contig55725_RC −0.605841 ESTs, Moderately similar to T50635 hypothetical protein DKFZp762L0311.1 [H. sapiens] Contig37015_RC −0.605780 ESTs, Weakly similar to UAS3_HUMAN UBASH3A PROTEIN [H. sapiens] AL157480 −0.604362 SH3BP1 SH3-domain binding protein 1 NM_005325 −0.604310 H1F1 H1 histone family, member 1 NM_001446 −0.604061 FABP7 fatty acid binding protein 7, brain Contig263_RC 0.603318 Homo sapiens cDNA: FLJ23000 fis, clone LNG00194 Contig8347_RC −0.603311 ESTs NM_002988 −0.603279 SCYA18 small inducible cytokine subfamily A (Cys-Cys), member 18, pulmonary and activation-regulated AF111849 0.603157 HELO1 homolog of yeast long chain polyunsaturated fatty acid elongation enzyme 2 NM_014700 0.603042 KIAA0665 KIAA0665 gene product NM_001814 −0.602988 CTSC cathepsin C AF116682 −0.602350 PRO2013 hypothetical protein PRO2013 AB037836 0.602024 KIAA1415 KIAA1415 protein AB002301 0.602005 KIAA0303 KIAA0303 protein NM_002996 −0.601841 SCYD1 small inducible cytokine subfamily D (Cys-X3-Cys), member 1 (fractalkine, neurotactin) NM_018410 −0.601765 DKFZp762E1312 hypothetical protein DKFZp762E1312 Contig49581_RC −0.601571 KIAA1350 KIAA1350 protein NM_003088 −0.601458 SNL singed (Drosophila)-like (sea urchin fascin homolog like) Contig47045_RC 0.601088 ESTs, Weakly similar to DP1_HUMAN POLYPOSIS LOCUS PROTEIN 1 [H. sapiens] NM_001806 −0.600954 CEBPG CCAAT/enhancer binding protein (C/EBP), gamma NM_004374 0.600766 COX6C cytochrome c oxidase subunit VIc Contig52641_RC 0.600132 ESTs, Weakly similar to CENB MOUSE MAJOR CENTROMERE AUTOANTIGEN B [M. musculus] NM_000100 −0.600127 CSTB cystatin B (stefin B) NM_002250 −0.600004 KCNN4 potassium intermediate/small conductance calcium-activated channel, subfamily N, member 4 AB033035 −0.599423 KIAA1209 KIAA1209 protein Contig53968_RC 0.599077 ESTs NM_002300 −0.598246 LDHB lactate dehydrogenase B NM_000507 0.598110 FBP1 fructose-1,6-bisphosphatase 1 NM_002053 −0.597756 GBP1 guanylate binding protein 1, interferon-inducible, 67 kD AB007883 0.597043 KIAA0423 KIAA0423 protein NM_004900 −0.597010 DJ742C19.2 phorbolin (similar to apolipoprotein B mRNA editing protein) NM_004480 0.596321 FUT8 fucosyltransferase 8 (alpha (1,6) fucosyltransferase) Contig35896_RC 0.596281 ESTs NM_020974 0.595173 CEGP1 CEGP1 protein NM_000662 0.595114 NAT1 N-acetyltransferase 1 (arylamine N- acetyltransferase) NM_006113 0.595017 VAV3 vav 3 oncogene NM_014865 −0.594928 KIAA0159 chromosome condensation-related SMC-associated protein 1 Contig55538_RC −0.594573 BA395L14.2 hypothetical protein bA395L14.2 NM_016056 0.594084 LOC51643 CGI-119 protein NM_003579 −0.594063 RAD54L RAD54 (S. cerevisiae)-like NM_014214 −0.593860 IMPA2 inositol(myo)-1(or 4)- monophosphatase 2 U79293 0.593793 Human clone 23948 mRNA sequence NM_005557 −0.593746 KRT16 keratin 16 (focal non-epidermolytic palmoplantar keratoderma) NM_002444 −0.592405 MSN moesin NM_003681 −0.592155 PDXK pyridoxal (pyridoxine, vitamin B6) kinase NM_006372 −0.591711 NSAP1 NS1-associated protein 1 NM_005218 −0.591192 DEFB1 defensin, beta 1 NM_004642 −0.591081 DOC1 deleted in oral cancer (mouse, homolog) 1 AL133074 0.590359 Homo sapiens cDNA: FLJ22139 fis, clone HEP20959 M73547 0.590317 D5S346 DNA segment, single copy probe LNS-CAI/LNS-CAII (deleted in polyposis Contig65663 0.590312 ESTs AL035297 −0.589728 H. sapiens gene from PAC 747L4 Contig35629_RC 0.589383 ESTs NM_019027 0.588862 FLJ20273 hypothetical protein NM_012425 −0.588804 Homo sapiens Ras suppressor protein 1 (RSU1), mRNA NM_020179 −0.588326 FN5 FN5 protein AF090913 −0.587275 TMSB10 thymosin, beta 10 NM_004176 0.587190 SREBF1 sterol regulatory element binding transcription factor 1 NM_016121 0.586941 LOC51133 NY-REN-45 antigen NM_014773 0.586871 KIAA0141 KIAA0141 gene product NM_019000 0.586677 FLJ20152 hypothetical protein NM_016243 0.585942 LOC51706 cytochrome b5 reductase 1 (B5R.1) NM_014274 −0.585815 ABP/ZF Alu-binding protein with zinc finger domain NM_018379 0.585497 FLJ11280 hypothetical protein FLJ11280 AL157431 −0.585077 DKFZp762A227 hypothetical protein DKFZp762A227 D38521 −0.584684 KIAA0077 KIAA0077 protein NM_002570 0.584272 PACE4 paired basic amino acid cleaving system 4 NM_001809 −0.584252 CENPA centromere protein A (17 kD) NM_003318 −0.583556 TTK TTK protein kinase NM_014325 −0.583555 CORO1C coronin, actin-binding protein, 1C NM_005667 0.583376 ZFP103 zinc finger protein homologous to Zfp103 in mouse NM_004354 0.582420 CCNG2 cyclin G2 NM_003670 0.582235 BHLHB2 basic helix-loop-helix domain containing, class B, 2 NM_001673 −0.581902 ASNS asparagine synthetase NM_001333 −0.581402 CTSL2 cathepsin L2 Contig54295_RC 0.581256 ESTs Contig33998_RC 0.581018 ESTs NM_006002 −0.580592 UCHL3 ubiquitin carboxyl-terminal esterase L3 (ubiquitin thiolesterase) NM_015392 0.580568 NPDC1 neural proliferation, differentiation and control, 1 NM_004866 0.580138 SCAMP1 secretory carrier membrane protein 1 Contig50391_RC 0.580071 ESTs NM_000592 0.579965 C4B complement component 4B Contig50802_RC 0.579881 ESTs Contig41635_RC −0.579468 ESTs NM_006845 −0.579339 KNSL6 kinesin-like 6 (mitotic centromere- associated kinesin) NM_003720 −0.579296 DSCR2 Down syndrome critical region gene 2 NM_000060 0.578967 BTD biotinidase AL050388 −0.578736 Homo sapiens mRNA; cDNA DKFZp564M2422 (from clone DKFZp564M2422); partial cds NM_003772 −0.578395 JRKL jerky (mouse) homolog-like NM_014398 −0.578388 TSC403 similar to lysosome-associated membrane glycoprotein NM_001280 0.578213 CIRBP cold inducible RNA-binding protein NM_001395 −0.577369 DUSP9 dual specificity phosphatase 9 NM_016229 −0.576290 LOC51700 cytochrome b5 reductase b5R.2 NM_006096 −0.575615 NDRG1 N-myc downstream regulated NM_001552 0.575438 IGFBP4 insulin-like growth factor-binding protein 4 NM_005558 −0.574818 LAD1 ladinin 1 Contig54534_RC 0.574784 Human glucose transporter pseudogene Contig1239_RC 0.573822 Human Chromosome 16 BAC clone CIT987SK-A-362G6 Contig57173_RC 0.573807 Homo sapiens mRNA for KIAA1737 protein, partial cds NM_004414 −0.573538 DSCR1 Down syndrome critical region gene 1 NM_021103 −0.572722 TMSB10 thymosin, beta 10 NM_002350 −0.571917 LYN v-yes-1 Yamaguchi sarcoma viral related oncogene homolog Contig51235_RC 0.571049 Homo sapiens cDNA: FLJ23388 fis, clone HEP17008 NM_013384 0.570987 TMSG1 tumor metastasis-suppressor NM_014399 0.570936 NET-6 tetraspan NET-6 protein Contig26022_RC −0.570851 ESTs AB023152 0.570561 KIAA0935 KIAA0935 protein NM_021077 −0.569944 NMB neuromedin B NM_003498 −0.569129 SNN stannin U17077 −0.568979 BENE BENE protein D86985 0.567698 KIAA0232 KIAA0232 gene product NM_006357 −0.567513 UBE2E3 ubiquitin-conjugating enzyme E2E 3 (homologous to yeast UBC4/5) AL049397 −0.567434 Homo sapiens mRNA; cDNA DKFZp586C1019 (from clone DKFZp586C1019) Contig64502 0.567433 ESTs, Weakly similar to unknown [M. musculus] Contig56298_RC −0.566892 FLJ13154 hypothetical protein FLJ13154 Contig46056_RC 0.566634 ESTs, Weakly similar to YZ28_HUMAN HYPOTHETICAL PROTEIN ZAP128 [H. sapiens] AF007153 0.566044 Homo sapiens clone 23736 mRNA sequence Contig1778_RC −0.565789 ESTs NM_017702 −0.565789 FLJ20186 hypothetical protein FLJ20186 Contig39226_RC 0.565761 Homo sapiens cDNA FLJ12187 fis, clone MAMMA1000831 NM_000168 0.564879 GLI3 GLI-Kruppel family member GLI3 (Greig cephalopolysyndactyly syndrome) Contig57609_RC 0.564751 ESTs, Weakly similar to T2D3_HUMAN TRANSCRIPTION INITIATION FACTOR TFIID 135 KDA SUBUNIT [H. sapiens] U45975 0.564602 PIB5PA phosphatidylinositol (4,5) bisphosphate 5-phosphatase, A AF038182 0.564596 Homo sapiens clone 23860 mRNA sequence Contig5348_RC 0.564480 ESTs, Weakly similar to 1607338A transcription factor BTF3a [H. sapiens] NM_001321 −0.564459 CSRP2 cysteine and glycine-rich protein 2 Contig25362_RC −0.563801 ESTs NM_001609 0.563782 ACADSB acyl-Coenzyme A dehydrogenase, short/branched chain Contig40146 0.563731 wi84e12.x1 NCI_CGAP_Kid12 Homo sapiens cDNA clone IMAGE: 2400046 3′ similar to SW: RASD_DICDI P03967 RAS- LIKE PROTEIN RASD;, mRNA sequence. NM_016002 0.563403 LOC51097 CGI-49 protein Contig34303_RC 0.563157 Homo sapiens cDNA: FLJ21517 fis, clone COL05829 Contig55883_RC 0.563141 ESTs NM_017961 0.562479 FLJ20813 hypothetical protein FLJ20813 M21551 −0.562340 NMB neuromedin B Contig3940_RC −0.561956 YWHAH tyrosine 3- monooxygenase/tryptophan 5- monooxygenase activation protein, eta polypeptide AB033111 −0.561746 KIAA1285 KIAA1285 protein Contig43410_RC 0.561678 ESTs Contig42006_RC −0.561677 ESTs Contig57272_RC 0.561228 ESTs G26403 −0.561068 YWHAH tyrosine 3- monooxygenase/tryptophan 5- monooxygenase activation protein, eta polypeptide NM_005915 −0.560813 MCM6 minichromosome maintenance deficient (mis5, S. pombe) 6 NM_003875 −0.560668 GMPS guanine monphosphate synthetase AK000142 0.559651 AK000142 Homo sapiens cDNA FLJ20135 fis, clone COL06818. NM_002709 −0.559621 PPP1CB protein phosphatase 1, catalytic subunit, beta isoform NM_001276 −0.558868 CHI3L1 chitinase 3-like 1 (cartilage glycoprotein-39) NM_002857 0.558862 PXF peroxisomal farnesylated protein Contig33815_RC −0.558741 FLJ22833 hypothetical protein FLJ22833 NM_003740 −0.558491 KCNK5 potassium channel, subfamily K, member 5 (TASK-2) Contig53646_RC 0.558455 ESTs NM_005538 −0.558350 INHBC inhibin, beta C NM_002111 0.557860 HD huntingtin (Huntington disease) NM_003683 −0.557807 D21S2056E DNA segment on chromosome 21 (unique) 2056 expressed sequence NM_003035 −0.557380 SIL TAL1 (SCL) interrupting locus Contig4388_RC −0.557216 Homo sapiens, Similar to integral membrane protein 3, clone MGC: 3011, mRNA, complete cds Contig38288_RC −0.556426 ESTs, Weakly similar to ISHUSS protein disulfide-isomerase [H. sapiens] NM_015417 0.556184 DKFZP434I114 DKFZP434I114 protein NM_015507 −0.556138 EGFL6 EGF-like-domain, multiple 6 AF279865 0.555951 KIF13B kinesin family member 13B Contig31288_RC −0.555754 ESTs NM_002966 −0.555620 S100A10 S100 calcium-binding protein A10 (annexin II ligand, calpactin I, light polypeptide (p11)) NM_017585 −0.555476 SLC2A6 solute carrier family 2 (facilitated glucose transporter), member 6 NM_013296 −0.555367 HSU54999 LGN protein NM_000224 0.554838 KRT18 keratin 18 Contig49270_RC −0.554593 KIAA1553 KIAA1553 protein NM_004848 −0.554538 ICB-1 basement membrane-induced gene NM_007275 0.554278 FUS1 lung cancer candidate NM_007044 −0.553550 KATNA1 katanin p60 (ATPase-containing) subunit A 1 Contig1829 0.553317 ESTs AF272357 0.553286 NPDC1 neural proliferation, differentiation and control, 1 Contig57584_RC −0.553080 Homo sapiens, Similar to gene rich cluster, C8 gene, clone MGC: 2577, mRNA, complete cds NM_003039 −0.552747 SLC2A5 solute carrier family 2 (facilitated glucose transporter), member 5 NM_014216 0.552321 ITPK1 inositol 1,3,4-triphosphate 5/6 kinase NM_007027 −0.552064 TOPBP1 topoisomerase (DNA) II binding protein AF118224 −0.551916 ST14 suppression of tumorigenicity 14 (colon carcinoma, matriptase, epithin) X75315 −0.551853 HSRNASEB seb4D NM_012101 −0.551824 ATDC ataxia-telangiectasia group D- associated protein AL157482 −0.551329 FLJ23399 hypothetical protein FLJ23399 NM_012474 −0.551150 UMPK uridine monophosphate kinase Contig57081_RC 0.551103 ESTs NM_006941 −0.551069 SOX10 SRY (sex determining region Y)-box 10 NM_004694 0.550932 SLC16A6 solute carrier family 16 (monocarboxylic acid transporters), member 6 Contig9541_RC 0.550680 ESTs Contig20617_RC 0.550546 ESTs NM_004252 0.550365 SLC9A3R1 solute carrier family 9 (sodium/hydrogen exchanger), isoform 3 regulatory factor 1 NM_015641 −0.550200 DKFZP586 testin B2022 NM_004336 −0.550164 BUB1 budding uninhibited by benzimidazoles 1 (yeast homolog) Contig39960_RC −0.549951 FLJ21079 hypothetical protein FLJ21079 NM_020686 0.549659 NPD009 NPD009 protein NM_002633 −0.549647 PGM1 phosphoglucomutase 1 Contig30480_RC 0.548932 ESTs NM_003479 0.548896 PTP4A2 protein tyrosine phosphatase type IVA, member 2 NM_001679 −0.548768 ATP1B3 ATPase, Na+/K+ transporting, beta 3 polypeptide NM_001124 −0.548601 ADM adrenomedullin NM_001216 −0.548375 CA9 carbonic anhydrase IX U58033 −0.548354 MTMR2 myotubularin related protein 2 NM_018389 −0.547875 FLJ11320 hypothetical protein FLJ11320 AF176012 0.547867 JDP1 J domain containing protein 1 Contig66705_RC −0.546926 ST5 suppression of tumorigenicity 5 NM_018194 0.546878 FLJ10724 hypothetical protein FLJ10724 NM_006851 −0.546823 RTVP1 glioma pathogenesis-related protein Contig53870_RC 0.546756 ESTs NM_002482 −0.546012 NASP nuclear autoantigenic sperm protein (histone-binding) NM_002292 0.545949 LAMB2 laminin, beta 2 (laminin S) NM_014696 −0.545758 KIAA0514 KIAA0514 gene product Contig49855 0.545517 ESTs AL117666 0.545203 DKFZP586O1624 DKFZP586O1624 protein NM_004701 −0.545185 CCNB2 cyclin B2 NM_007050 0.544890 PTPRT protein tyrosine phosphatase, receptor type, T NM_000414 0.544778 HSD17B4 hydroxysteroid (17-beta) dehydrogenase 4 Contig52398_RC −0.544775 Homo sapiens cDNA: FLJ21950 fis, clone HEP04949 AB007916 0.544496 KIAA0447 KIAA0447 gene product Contig66219_RC 0.544467 FLJ22402 hypothetical protein FLJ22402 D87453 0.544145 KIAA0264 KIAA0264 protein NM_015515 −0.543929 DKFZP434G032 DKFZP434G032 protein NM_001530 −0.543898 HIF1A hypoxia-inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor) NM_004109 −0.543893 FDX1 ferredoxin 1 NM_000381 −0.543871 MID1 midline 1 (Opitz/BBB syndrome) Contig43983_RC 0.543523 CS2 calsyntenin-2 AL137761 0.543371 Homo sapiens mRNA; cDNA DKFZp586L2424 (from clone DKFZp586L2424) NM_005764 −0.543175 DD96 epithelial protein up-regulated in carcinoma, membrane associated protein 17 Contig1838_RC 0.542996 Homo sapiens cDNA: FLJ22722 fis, clone HSI14444 NM_006670 0.542932 5T4 5T4 oncofetal trophoblast glycoprotein Contig28552_RC −0.542617 Homo sapiens mRNA; cDNA DKFZp434C0931 (from clone DKFZp434C0931); partial cds Contig14284_RC 0.542224 ESTs NM_006290 −0.542115 TNFAIP3 tumor necrosis factor, alpha-induced protein 3 AL050372 0.541463 Homo sapiens mRNA; cDNA DKFZp434A091 (from clone DKFZp434A091); partial cds NM_014181 −0.541095 HSPC159 HSPC159 protein Contig37141_RC 0.540990 Homo sapiens cDNA: FLJ23582 fis, clone LNG 13759 NM_000947 −0.540621 PRIM2A primase, polypeptide 2A (58 kD) NM_002136 0.540572 HNRPA1 heterogeneous nuclear ribonucleoprotein A1 NM_004494 −0.540543 HDGF hepatoma-derived growth factor (high-mobility group protein 1-like) Contig38983_RC 0.540526 ESTs Contig27882_RC −0.540506 ESTs Z11887 −0.540020 MMP7 matrix metalloproteinase 7 (matrilysin, uterine) NM_014575 −0.539725 SCHIP-1 schwannomin interacting protein 1 Contig38170_RC 0.539708 ESTs Contig44064_RC 0.539403 ESTs U68385 0.539395 MEIS3 Meis (mouse) homolog 3 Contig51967_RC 0.538952 ESTs Contig37562_RC 0.538657 ESTs, Weakly similar to transformation-related protein [H. sapiens] Contig40500_RC 0.538582 ESTs, Weakly similar to unnamed protein product [H. sapiens] Contig1129_RC 0.538339 ESTs NM_002184 0.538185 IL6ST interleukin 6 signal transducer (gp130, oncostatin M receptor) AL049381 0.538041 Homo sapiens cDNA FLJ12900 fis, clone NT2RP2004321 NM_002189 −0.537867 IL15RA interleukin 15 receptor, alpha NM_012110 −0.537562 CHIC2 cystein-rich hydrophobic domain 2 AB040881 −0.537473 KIAA1448 KIAA1448 protein NM_016577 −0.537430 RAB6B RAB6B, member RAS oncogene family NM_001745 0.536940 CAMLG calcium modulating ligand NM_005742 −0.536738 P5 protein disulfide isomerase-related protein AB011132 0.536345 KIAA0560 KIAA0560 gene product Contig54898_RC 0.536094 PNN pinin, desmosome associated protein Contig45049_RC −0.536043 FUT4 fucosyltransferase 4 (alpha (1,3) fucosyltransferase, myeloid-specific) NM_006864 −0.535924 LILRB3 leukocyte immunoglobulin-like receptor, subfamily B (with TM and ITIM domains), member 3 Contig53242_RC −0.535909 Homo sapiens cDNA FLJ11436 fis, clone HEMBA1001213 NM_005544 0.535712 IRS1 insulin receptor substrate 1 Contig47456_RC 0.535493 CACNA1D calcium channel, voltage- dependent, L type, alpha 1D subunit Contig42751_RC −0.535469 ESTs Contig29126_RC −0.535186 ESTs NM_012391 0.535067 PDEF prostate epithelium-specific Ets transcription factor NM_012429 0.534974 SEC14L2 SEC14 (S. cerevisiae)-like 2 NM_018171 0.534898 FLJ10659 hypothetical protein FLJ10659 Contig53047_RC −0.534773 TTYH1 tweety (Drosophila) homolog 1 Contig54968_RC 0.534754 Homo sapiens cDNA FLJ13558 fis, clone PLACE1007743 Contig2099_RC −0.534694 KIAA1691 KIAA1691 protein NM_005264 0.534057 GFRA1 GDNF family receptor alpha 1 NM_014036 −0.533638 SBBI42 BCM-like membrane protein precursor NM_018101 −0.533473 FLJ10468 hypothetical protein FLJ10468 Contig56765_RC 0.533442 ESTs, Moderately similar to K02E10.2 [C. elegans] AB006746 −0.533400 PLSCR1 phospholipid scramblase 1 NM_001089 0.533350 ABCA3 ATP-binding cassette, sub-family A (ABC1), member 3 NM_018188 −0.533132 FJ10709 hypothetical protein FLJ10709 X94232 −0.532925 MAPRE2 microtubule-associated protein, RP/EB family, member 2 AF234532 −0.532910 MYO10 myosin X Contig292_RC 0.532853 FLJ22386 hypothetical protein FLJ22386 NM_000101 −0.532767 CYBA cytochrome b-245, alpha polypeptide Contig47814_RC −0.532656 HHGP HHGP protein NM_014320 −0.532430 SOUL putative heme-binding protein NM_020347 0.531976 LZTFL1 leucine zipper transcription factor- like 1 NM_004323 0.531936 BAG1 BCL2-associated athanogene Contig50850_RC −0.531914 ESTs Contig11648_RC 0.531704 ESTs NM_018131 −0.531559 FLJ10540 hypothetical protein FLJ10540 NM_004688 −0.531329 NMI N-myc (and STAT) interactor NM_014870 0.531101 KIAA0478 KIAA0478 gene product Contig31424_RC 0.530720 ESTs NM_000874 −0.530545 IFNAR2 interferon (alpha, beta and omega) receptor 2 Contig50588_RC 0.530145 ESTs NM_016463 0.529998 HSPC195 hypothetical protein NM_013324 0.529966 CISH cytokine inducible SH2-containing protein NM_006705 0.529840 GADD45G growth arrest and DNA-damage- inducible, gamma Contig38901_RC −0.529747 ESTs NM_004184 −0.529635 WARS tryptophanyl-tRNA synthetase NM_015955 −0.529538 LOC51072 CGI-27 protein AF151810 0.529416 CGI-52 similar to phosphatidylcholine transfer protein 2 NM_002164 −0.529117 INDO indoleamine-pyrrole 2,3 dioxygenase NM_004267 −0.528679 CHST2 carbohydrate (chondroitin 6/keratan) sulfotransferase 2 Contig32185_RC −0.528529 Homo sapiens cDNA FLJ13997 fis, clone Y79AA1002220 NM_004154 −0.528343 P2RY6 pyrimidinergic receptor P2Y, G- protein coupled, 6 NM_005235 0.528294 ERBB4 v-erb-a avian erythroblastic leukemia viral oncogene homolog- like 4 Contig40208_RC −0.528062 LOC56938 transcription factor BMAL2 NM_013262 0.527297 MIR myosin regulatory light chain interacting protein NM_003034 −0.527148 SIAT8A sialyltransferase 8 (alpha-N- acetylneuraminate: alpha-2,8- sialytransferase, GD3 synthase) A NM_004556 −0.527146 NFKBIE nuclear factor of kappa light polypeptide gene enhancer in B- cells inhibitor, epsilon NM_002046 −0.527051 GAPD glyceraldehyde-3-phosphate dehydrogenase NM_001905 −0.526986 CTPS CTP synthase Contig42402_RC 0.526852 ESTs NM_014272 −0.526283 ADAMTS7 a disintegrin-like and metalloprotease (reprolysin type) with thrombospondin type 1 motif, 7 AF076612 0.526205 CHRD chordin Contig57725_RC −0.526122 Homo sapiens mRNA for HMG-box transcription factor TCF-3, complete cds Contig42041_RC −0.525877 ESTs Contig44656_RC −0.525868 ESTs, Highly similar to S02392 alpha-2-macroglobulin receptor precursor [H. sapiens] NM_018004 −0.525610 FLJ10134 hypothetical protein FLJ10134 Contig56434_RC 0.525510 Homo sapiens cDNA FLJ13603 fis, clone PLACE 1010270 D25328 −0.525504 PFKP phosphofructokinase, platelet Contig55950_RC −0.525358 FLJ22329 hypothetical protein FLJ22329 NM_002648 −0.525211 PIM1 pim-1 oncogene AL157505 0.525186 Homo sapiens mRNA; cDNA DKFZp586P1124 (from clone DKFZp586P1124) AF061034 −0.525185 FIP2 Homo sapiens FIP2 alternatively translated mRNA, complete cds. NM_014721 −0.525102 KIAA0680 KIAA0680 gene product NM_001634 −0.525030 AMD1 S-adenosylmethionine decarboxylase 1 NM_006304 −0.524911 DSS1 Deleted in split-hand/split-foot 1 region Contig37778_RC 0.524667 ESTs, Highly similar to HLHUSB MHC class II histocompatibility antigen HLA-DP alpha-1 chain precursor [H. sapiens] NM_003099 0.524339 SNX1 sorting nexin 1 AL079298 0.523774 MCCC2 methylcrotonoyl-Coenzyme A carboxylase 2 (beta) NM_019013 −0.523663 FLJ10156 hypothetical protein NM_000397 −0.523293 CYBB cytochrome b-245, beta polypeptide (chronic granulomatous disease) NM_014811 0.523132 KIAA0649 KIAA0649 gene product Contig20600_RC 0.523072 ESTs NM_005190 −0.522710 CCNC cyclin C AL161960 −0.522574 FLJ21324 hypothetical protein FLJ21324 AL117502 0.522280 Homo sapiens mRNA; cDNA DKFZp434D0935 (from clone DKFZp434D0935) AF131753 −0.522245 Homo sapiens clone 24859 mRNA sequence NM_000320 0.521974 QDPR quinoid dihydropteridine reductase NM_002115 −0.521870 HK3 hexokinase 3 (white cell) NM_006460 0.521696 HIS1 HMBA-inducible NM_018683 −0.521679 ZNF313 zinc finger protein 313 NM_004305 −0.521539 BIN1 bridging integrator 1 NM_006770 −0.521538 MARCO macrophage receptor with collagenous structure NM_001166 −0.521530 BIRC2 baculoviral IAP repeat-containing 2 D42047 0.521522 KIAA0089 KIAA0089 protein NM_016235 −0.521298 GPRC5B G protein-coupled receptor, family C, group 5, member B NM_004504 −0.521189 HRB HIV-1 Rev binding protein NM_002727 −0.521146 PRG1 proteoglycan 1, secretory granule AB029031 −0.520761 KIAA1108 KIAA1108 protein NM_005556 −0.520692 KRT7 keratin 7 NM_018031 0.520600 WDR6 WD repeat domain 6 AL117523 −0.520579 KIAA1053 KIAA1053 protein NM_004515 −0.520363 ILF2 interleukin enhancer binding factor 2, 45 kD NM_004708 −0.519935 PDCD5 programmed cell death 5 NM_005935 0.519765 MLLT2 myeloid/lymphoid or mixed-lineage leukemia (trithorax (Drosophila) homolog); translocated to, 2 Contig49289_RC −0.519546 Homo sapiens mRNA; cDNA DKFZp586J1119 (from clone DKFZp586J1119); complete cds NM_000211 −0.519342 ITGB2 integrin, beta 2 (antigen CD18 (p95), lymphocyte function-associated antigen 1; macrophage antigen 1 (mac-1) beta subunit) AL079276 0.519207 LOC58495 putative zinc finger protein from EUROIMAGE 566589 Contig57825_RC 0.519041 ESTs NM_002466 −0.518911 MYBL2 v-myb avian myeloblastosis viral oncogene homolog-like 2 NM_016072 −0.518802 LOC51026 CGI-141 protein AB007950 −0.518699 KIAA0481 KIAA0481 gene product NM_001550 −0.518549 IFRD1 interferon-related developmental regulator 1 AF155120 −0.518221 UBE2V1 ubiquitin-conjugating enzyme E2 variant 1 Contig49849_RC 0.517983 ESTs, Weakly similar to AF188706 1 g20 protein [H. sapiens] NM_016625 −0.517936 LOC51319 hypothetical protein NM_004049 −0.517862 BCL2A1 BCL2-related protein A1 Contig50719_RC 0.517740 ESTs D80010 −0.517620 LPIN1 lipin 1 NM_000299 −0.517405 PKP1 plakophilin 1 (ectodermal dysplasia/skin fragility syndrome) AL049365 0.517080 FTL ferritin, light polypeptide Contig65227 0.517003 ESTs NM_004865 −0.516808 TBPL1 TBP-like 1 Contig54813_RC 0.516246 FLJ13962 hypothetical protein FLJ13962 NM_003494 −0.516221 DYSF dysferlin, limb girdle muscular dystrophy 2B (autosomal recessive) NM_004431 −0.516212 EPHA2 EphA2 AL117600 −0.516067 DKFZP564J0863 DKFZP564J0863 protein AL080209 −0.516037 DKFZP586F2423 hypothetical protein DKFZp586F2423 NM_000135 −0.515613 FANCA Fanconi anemia, complementation group A NM_000050 −0.515494 ASS argininosuccinate synthetase NM_001830 −0.515439 CLCN4 chloride channel 4 NM_018234 −0.515365 FLJ10829 hypothetical protein FLJ10829 Contig53307_RC 0.515328 ESTs, Highly similar to KIAA1437 protein [H. sapiens] AL117617 −0.515141 Homo sapiens mRNA; cDNA DKFZp564H0764 (from clone DKFZp564H0764) NM_002906 −0.515098 RDX radixin NM_003360 −0.514427 UGT8 UDP glycosyltransferase 8 (UDP- galactose ceramide galactosyltransferase) NM_018478 0.514332 HSMNP1 uncharacterized hypothalamus protein HSMNP1 M90657 −0.513908 TM4SF1 transmembrane 4 superfamily member 1 NM_014967 0.513793 KIAA1018 KIAA1018 protein Contig1462_RC 0.513604 C11ORF15 chromosome 11 open reading frame 15 Contig37287_RC −0.513324 ESTs NM_000355 −0.513225 TCN2 transcobalamin II; macrocytic anemia AB037756 0.512914 KIAA1335 hypothetical protein KIAA1335 Contig842_RC −0.512880 ESTs NM_018186 −0.512878 FLJ10706 hypothetical protein FLJ10706 NM_014668 0.512746 KIAA0575 KIAA0575 gene product NM_003226 0.512611 TFF3 trefoil factor 3 (intestinal) Contig56457_RC −0.512548 TMEFF1 transmembrane protein with EGF- like and two follistatin-like domains 1 AL050367 −0.511999 Homo sapiens mRNA; cDNA DKFZp564A026 (from clone DKFZp564A026) NM_014791 −0.511963 KIAA0175 KIAA0175 gene product Contig36312_RC 0.511794 ESTs NM_004811 −0.511447 LPXN leupaxin Contig67182_RC −0.511416 ESTs, Highly similar to epithelial V- like antigen precursor [H. sapiens] Contig52723_RC −0.511134 ESTs Contig17105_RC −0.511072 Homo sapiens mRNA for putative cytoplasmatic protein (ORF1-FL21) NM_014449 0.511023 A protein “A” Contig52957_RC 0.510815 ESTs Contig49388_RC 0.510582 FLJ13322 hypothetical protein FLJ13322 NM_017786 0.510557 FLJ20366 hypothetical protein FLJ20366 AL157476 0.510478 Homo sapiens mRNA; cDNA DKFZp761C082 (from clone DKFZp761C082) NM_001919 0.510242 DCI dodecenoyl-Coenzyme A delta isomerase (3,2 trans-enoyl- Coenzyme A isomerase) NM_000268 −0.510165 NF2 neurofibromin 2 (bilateral acoustic neuroma) NM_016210 0.510018 LOC51161 g20 protein Contig45816_RC −0.509977 ESTs NM_003953 −0.509969 MPZL1 myelin protein zero-like 1 NM_000057 −0.509669 BLM Bloom syndrome NM_014452 −0.509473 DR6 death receptor 6 Contig45156_RC 0.509284 ESTs, Moderately similar to motor domain of KIF12 [M. musculus] NM_006943 0.509149 SOX22 SRY (sex determining region Y)-box 22 NM_000594 −0.509012 TNF tumor necrosis factor (TNF superfamily, member 2) AL137316 −0.508353 KIAA1609 KIAA1609 protein NM_000557 −0.508325 GDF5 growth differentiation factor 5 (cartilage-derived morphogenetic protein-1) NM_018685 −0.508307 ANLN anillin (Drosophila Scraps homolog), actin binding protein Contig53401_RC 0.508189 ESTs NM_014364 −0.508170 GAPDS glyceraldehyde-3-phosphate dehydrogenase, testis-specific Contig50297_RC 0.508137 ESTs, Moderately similar to ALU8_HUMAN ALU SUBFAMILY SX SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Contig51800 0.507891 ESTs, Weakly similar to ALU6_HUMAN ALU SUBFAMILY SP SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Contig49098_RC −0.507716 MGC4090 hypothetical protein MGC4090 NM_002985 −0.507554 SCYA5 small inducible cytokine A5 (RANTES) AB007899 0.507439 KIAA0439 KIAA0439 protein; homolog of yeast ubiquitin-protein ligase Rsp5 AL110139 0.507145 Homo sapiens mRNA; cDNA DKFZp564O1763 (from clone DKFZp564O1763) Contig51117_RC 0.507001 ESTs NM_017660 −0.506768 FLJ20085 hypothetical protein FLJ20085 NM_018000 0.506686 FLJ10116 hypothetical protein FLJ10116 NM_005555 −0.506516 KRT6B keratin 6B NM_005582 −0.506462 LY64 lymphocyte antigen 64 (mouse) homolog, radioprotective, 105 kD Contig47405_RC 0.506202 ESTs NM_014808 0.506173 KIAA0793 KIAA0793 gene product NM_004938 −0.506121 DAPK1 death-associated protein kinase 1 NM_020659 −0.505793 TTYH1 tweety (Drosophila) homolog 1 NM_006227 −0.505604 PLTP phospholipid transfer protein NM_014268 −0.505412 MAPRE2 microtubule-associated protein, RP/EB family, member 2 NM_004711 0.504849 SYNGR1 synaptogyrin 1 NM_004418 −0.504497 DUSP2 dual specificity phosphatase 2 NM_003508 −0.504475 FZD9 frizzled (Drosophila) homolog 9

[0088] TABLE 3 430 gene markers that distinguish BRCA1-related tumor samples from sporadic tumor samples GenBank Accession Number SEQ ID NO AB002301 SEQ ID NO 4 AB004857 SEQ ID NO 8 AB007458 SEQ ID NO 12 AB014534 SEQ ID NO 29 AB018305 SEQ ID NO 34 AB020677 SEQ ID NO 36 AB020689 SEQ ID NO 37 AB023151 SEQ ID NO 41 AB023163 SEQ ID NO 43 AB028986 SEQ ID NO 48 AB029025 SEQ ID NO 50 AB032966 SEQ ID NO 53 AB032988 SEQ ID NO 57 AB033049 SEQ ID NO 63 AB033055 SEQ ID NO 66 AB037742 SEQ ID NO 73 AB041269 SEQ ID NO 96 AF000974 SEQ ID NO 97 AF042838 SEQ ID NO 111 AF052155 SEQ ID NO 119 AF055084 SEQ ID NO 125 AF063725 SEQ ID NO 129 AF070536 SEQ ID NO 133 AF070617 SEQ ID NO 135 AF073299 SEQ ID NO 136 AF079529 SEQ ID NO 140 AF090353 SEQ ID NO 141 AF116238 SEQ ID NO 155 AF151810 SEQ ID NO 171 AF220492 SEQ ID NO 185 AJ224741 SEQ ID NO 196 AJ250475 SEQ ID NO 201 AJ270996 SEQ ID NO 202 AJ272057 SEQ ID NO 203 AK000174 SEQ ID NO 211 AK000617 SEQ ID NO 215 AK000959 SEQ ID NO 222 AK001438 SEQ ID NO 229 AK001838 SEQ ID NO 233 AK002107 SEQ ID NO 238 AK002197 SEQ ID NO 239 AL035297 SEQ ID NO 241 AL049346 SEQ ID NO 243 AL049370 SEQ ID NO 245 AL049667 SEQ ID NO 249 AL080222 SEQ ID NO 276 AL096737 SEQ ID NO 279 AL110163 SEQ ID NO 282 AL133057 SEQ ID NO 300 AL133096 SEQ ID NO 302 AL133572 SEQ ID NO 305 AL133619 SEQ ID NO 307 AL133623 SEQ ID NO 309 AL137347 SEQ ID NO 320 AL137381 SEQ ID NO 322 AL137461 SEQ ID NO 325 AL137540 SEQ ID NO 328 AL137555 SEQ ID NO 329 AL137638 SEQ ID NO 332 AL137639 SEQ ID NO 333 AL137663 SEQ ID NO 334 AL137761 SEQ ID NO 339 AL157431 SEQ ID NO 340 AL161960 SEQ ID NO 351 AL355708 SEQ ID NO 353 AL359053 SEQ ID NO 354 D26488 SEQ ID NO 359 D38521 SEQ ID NO 361 D50914 SEQ ID NO 367 D80001 SEQ ID NO 369 G26403 SEQ ID NO 380 K02276 SEQ ID NO 383 M21551 SEQ ID NO 394 M27749 SEQ ID NO 397 M28170 SEQ ID NO 398 M73547 SEQ ID NO 409 M80899 SEQ ID NO 411 NM_000067 SEQ ID NO 423 NM_000087 SEQ ID NO 427 NM_000090 SEQ ID NO 428 NM_000165 SEQ ID NO 444 NM_000168 SEQ ID NO 445 NM_000196 SEQ ID NO 449 NM_000269 SEQ ID NO 457 NM_000310 SEQ ID NO 466 NM_000396 SEQ ID NO 479 NM_000397 SEQ ID NO 480 NM_000597 SEQ ID NO 502 NM_000636 SEQ ID NO 509 NM_000888 SEQ ID NO 535 NM_000903 SEQ ID NO 536 NM_000930 SEQ ID NO 540 NM_000931 SEQ ID NO 541 NM_000969 SEQ ID NO 547 NM_000984 SEQ ID NO 548 NM_001026 SEQ ID NO 552 NM_001054 SEQ ID NO 554 NM_001179 SEQ ID NO 567 NM_001184 SEQ ID NO 568 NM_001204 SEQ ID NO 571 NM_001206 SEQ ID NO 572 NM_001218 SEQ ID NO 575 NM_001275 SEQ ID NO 586 NM_001394 SEQ ID NO 602 NM_001424 SEQ ID NO 605 NM_001448 SEQ ID NO 610 NM_001504 SEQ ID NO 620 NM_001553 SEQ ID NO 630 NM_001674 SEQ ID NO 646 NM_001675 SEQ ID NO 647 NM_001725 SEQ ID NO 652 NM_001740 SEQ ID NO 656 NM_001756 SEQ ID NO 659 NM_001770 SEQ ID NO 664 NM_001797 SEQ ID NO 670 NM_001845 SEQ ID NO 680 NM_001873 SEQ ID NO 684 NM_001888 SEQ ID NO 687 NM_001892 SEQ ID NO 688 NM_001919 SEQ ID NO 694 NM_001946 SEQ ID NO 698 NM_001953 SEQ ID NO 699 NM_001960 SEQ ID NO 704 NM_001985 SEQ ID NO 709 NM_002023 SEQ ID NO 712 NM_002051 SEQ ID NO 716 NM_002053 SEQ ID NO 717 NM_002164 SEQ ID NO 734 NM_002200 SEQ ID NO 739 NM_002201 SEQ ID NO 740 NM_002213 SEQ ID NO 741 NM_002250 SEQ ID NO 747 NM_002512 SEQ ID NO 780 NM_002542 SEQ ID NO 784 NM_002561 SEQ ID NO 786 NM_002615 SEQ ID NO 793 NM_002686 SEQ ID NO 803 NM_002709 SEQ ID NO 806 NM_002742 SEQ ID NO 812 NM_002775 SEQ ID NO 815 NM_002975 SEQ ID NO 848 NM_002982 SEQ ID NO 849 NM_003104 SEQ ID NO 870 NM_003118 SEQ ID NO 872 NM_003144 SEQ ID NO 876 NM_003165 SEQ ID NO 882 NM_003197 SEQ ID NO 885 NM_003202 SEQ ID NO 886 NM_003217 SEQ ID NO 888 NM_003283 SEQ ID NO 898 NM_003462 SEQ ID NO 911 NM_003500 SEQ ID NO 918 NM_003561 SEQ ID NO 925 NM_003607 SEQ ID NO 930 NM_003633 SEQ ID NO 933 NM_003641 SEQ ID NO 934 NM_003683 SEQ ID NO 943 NM_003729 SEQ ID NO 949 NM_003793 SEQ ID NO 954 NM_003829 SEQ ID NO 958 NM_003866 SEQ ID NO 961 NM_003904 SEQ ID NO 967 NM_003953 SEQ ID NO 974 NM_004024 SEQ ID NO 982 NM_004053 SEQ ID NO 986 NM_004295 SEQ ID NO 1014 NM_004438 SEQ ID NO 1038 NM_004559 SEQ ID NO 1057 NM_004616 SEQ ID NO 1065 NM_004741 SEQ ID NO 1080 NM_004772 SEQ ID NO 1084 NM_004791 SEQ ID NO 1086 NM_004848 SEQ ID NO 1094 NM_004866 SEQ ID NO 1097 NM_005128 SEQ ID NO 1121 NM_005148 SEQ ID NO 1124 NM_005196 SEQ ID NO 1127 NM_005326 SEQ ID NO 1140 NM_005518 SEQ ID NO 1161 NM_005538 SEQ ID NO 1163 NM_005557 SEQ ID NO 1170 NM_005718 SEQ ID NO 1189 NM_005804 SEQ ID NO 1201 NM_005824 SEQ ID NO 1203 NM_005935 SEQ ID NO 1220 NM_006002 SEQ ID NO 1225 NM_006148 SEQ ID NO 1249 NM_006235 SEQ ID NO 1257 NM_006271 SEQ ID NO 1261 NM_006287 SEQ ID NO 1264 NM_006296 SEQ ID NO 1267 NM_006378 SEQ ID NO 1275 NM_006461 SEQ ID NO 1287 NM_006573 SEQ ID NO 1300 NM_006622 SEQ ID NO 1302 NM_006696 SEQ ID NO 1308 NM_006769 SEQ ID NO 1316 NM_006787 SEQ ID NO 1319 NM_006875 SEQ ID NO 1334 NM_006885 SEQ ID NO 1335 NM_006918 SEQ ID NO 1339 NM_006923 SEQ ID NO 1340 NM_006941 SEQ ID NO 1342 NM_007070 SEQ ID NO 1354 NM_007088 SEQ ID NO 1356 NM_007146 SEQ ID NO 1358 NM_007173 SEQ ID NO 1359 NM_007246 SEQ ID NO 1366 NM_007358 SEQ ID NO 1374 NM_012135 SEQ ID NO 1385 NM_012151 SEQ ID NO 1387 NM_012258 SEQ ID NO 1396 NM_012317 SEQ ID NO 1399 NM_012337 SEQ ID NO 1403 NM_012339 SEQ ID NO 1404 NM_012391 SEQ ID NO 1406 NM_012428 SEQ ID NO 1412 NM_013233 SEQ ID NO 1418 NM_013253 SEQ ID NO 1422 NM_013262 SEQ ID NO 1425 NM_013372 SEQ ID NO 1434 NM_013378 SEQ ID NO 1435 NM_014096 SEQ ID NO 1450 NM_014242 SEQ ID NO 1464 NM_014314 SEQ ID NO 1472 NM_014398 SEQ ID NO 1486 NM_014402 SEQ ID NO 1488 NM_014476 SEQ ID NO 1496 NM_014521 SEQ ID NO 1499 NM_014585 SEQ ID NO 1504 NM_014597 SEQ ID NO 1506 NM_014642 SEQ ID NO 1510 NM_014679 SEQ ID NO 1517 NM_014680 SEQ ID NO 1518 NM_014700 SEQ ID NO 1520 NM_014723 SEQ ID NO 1523 NM_014770 SEQ ID NO 1530 NM_014785 SEQ ID NO 1534 NM_014817 SEQ ID NO 1539 NM_014840 SEQ ID NO 1541 NM_014878 SEQ ID NO 1546 NM_015493 SEQ ID NO 1564 NM_015523 SEQ ID NO 1568 NM_015544 SEQ ID NO 1570 NM_015623 SEQ ID NO 1572 NM_015640 SEQ ID NO 1573 NM_015721 SEQ ID NO 1576 NM_015881 SEQ ID NO 1577 NM_015937 SEQ ID NO 1582 NM_015964 SEQ ID NO 1586 NM_015984 SEQ ID NO 1587 NM_016000 SEQ ID NO 1591 NM_016018 SEQ ID NO 1593 NM_016066 SEQ ID NO 1601 NM_016073 SEQ ID NO 1603 NM_016081 SEQ ID NO 1604 NM_016140 SEQ ID NO 1611 NM_016223 SEQ ID NO 1622 NM_016267 SEQ ID NO 1629 NM_016307 SEQ ID NO 1633 NM_016364 SEQ ID NO 1639 NM_016373 SEQ ID NO 1640 NM_016459 SEQ ID NO 1646 NM_016471 SEQ ID NO 1648 NM_016548 SEQ ID NO 1654 NM_016620 SEQ ID NO 1662 NM_016820 SEQ ID NO 1674 NM_017423 SEQ ID NO 1678 NM_017709 SEQ ID NO 1698 NM_017732 SEQ ID NO 1700 NM_017734 SEQ ID NO 1702 NM_017750 SEQ ID NO 1704 NM_017763 SEQ ID NO 1706 NM_017782 SEQ ID NO 1710 NM_017816 SEQ ID NO 1714 NM_018043 SEQ ID NO 1730 NM_018072 SEQ ID NO 1734 NM_018093 SEQ ID NO 1738 NM_018103 SEQ ID NO 1742 NM_018171 SEQ ID NO 1751 NM_018187 SEQ ID NO 1755 NM_018188 SEQ ID NO 1756 NM_018222 SEQ ID NO 1761 NM_018228 SEQ ID NO 1762 NM_018373 SEQ ID NO 1777 NM_018390 SEQ ID NO 1781 NM_018422 SEQ ID NO 1784 NM_018509 SEQ ID NO 1792 NM_018584 SEQ ID NO 1796 NM_018653 SEQ ID NO 1797 NM_018660 SEQ ID NO 1798 NM_018683 SEQ ID NO 1799 NM_019049 SEQ ID NO 1814 NM_019063 SEQ ID NO 1815 NM_020150 SEQ ID NO 1823 NM_020987 SEQ ID NO 1848 NM_021095 SEQ ID NO 1855 NM_021242 SEQ ID NO 1867 U41387 SEQ ID NO 1877 U45975 SEQ ID NO 1878 U58033 SEQ ID NO 1881 U67784 SEQ ID NO 1884 U68385 SEQ ID NO 1885 U80736 SEQ ID NO 1890 X00437 SEQ ID NO 1899 X07203 SEQ ID NO 1904 X16302 SEQ ID NO 1907 X51630 SEQ ID NO 1908 X57809 SEQ ID NO 1912 X57819 SEQ ID NO 1913 X58529 SEQ ID NO 1914 X66087 SEQ ID NO 1916 X69150 SEQ ID NO 1917 X72475 SEQ ID NO 1918 X74794 SEQ ID NO 1920 X75315 SEQ ID NO 1921 X84340 SEQ ID NO 1925 X98260 SEQ ID NO 1928 Y07512 SEQ ID NO 1931 Y14737 SEQ ID NO 1932 Z34893 SEQ ID NO 1934 Contig237_RC SEQ ID NO 1940 Contig292_RC SEQ ID NO 1942 Contig372_RC SEQ ID NO 1943 Contig756_RC SEQ ID NO 1955 Contig842_RC SEQ ID NO 1958 Contig1632_RC SEQ ID NO 1977 Contig1826_RC SEQ ID NO 1980 Contig2237_RC SEQ ID NO 1988 Contig2915_RC SEQ ID NO 2003 Contig3164_RC SEQ ID NO 2007 Contig3252_RC SEQ ID NO 2008 Contig3940_RC SEQ ID NO 2018 Contig9259_RC SEQ ID NO 2039 Contig10268_RC SEQ ID NO 2041 Contig10437_RC SEQ ID NO 2043 Contig10973_RC SEQ ID NO 2044 Contig14390_RC SEQ ID NO 2054 Contig16453_RC SEQ ID NO 2060 Contig16759_RC SEQ ID NO 2061 Contig19551 SEQ ID NO 2070 Contig24541_RC SEQ ID NO 2088 Contig25362_RC SEQ ID NO 2093 Contig25617_RC SEQ ID NO 2094 Contig25722_RC SEQ ID NO 2096 Contig26022_RC SEQ ID NO 2099 Contig27915_RC SEQ ID NO 2114 Contig28081_RC SEQ ID NO 2116 Contig28179_RC SEQ ID NO 2118 Contig28550_RC SEQ ID NO 2119 Contig29639_RC SEQ ID NO 2127 Contig29647_RC SEQ ID NO 2128 Contig30092_RC SEQ ID NO 2130 Contig30209_RC SEQ ID NO 2132 Contig32185_RC SEQ ID NO 2156 Contig32798_RC SEQ ID NO 2161 Contig33230_RC SEQ ID NO 2163 Contig33394_RC SEQ ID NO 2165 Contig36323_RC SEQ ID NO 2197 Contig36761_RC SEQ ID NO 2201 Contig37141_RC SEQ ID NO 2209 Contig37778_RC SEQ ID NO 2218 Contig38285_RC SEQ ID NO 2222 Contig38520_RC SEQ ID NO 2225 Contig38901_RC SEQ ID NO 2232 Contig39826_RC SEQ ID NO 2241 Contig40212_RC SEQ ID NO 2251 Contig40712_RC SEQ ID NO 2257 Contig41402_RC SEQ ID NO 2265 Contig41635_RC SEQ ID NO 2272 Contig42006_RC SEQ ID NO 2280 Contig42220_RC SEQ ID NO 2286 Contig42306_RC SEQ ID NO 2287 Contig43918_RC SEQ ID NO 2312 Contig44195_RC SEQ ID NO 2316 Contig44265_RC SEQ ID NO 2318 Contig44278_RC SEQ ID NO 2319 Contig44757_RC SEQ ID NO 2329 Contig45588_RC SEQ ID NO 2349 Contig46262_RC SEQ ID NO 2361 Contig46288_RC SEQ ID NO 2362 Contig46343_RC SEQ ID NO 2363 Contig46452_RC SEQ ID NO 2366 Contig46868_RC SEQ ID NO 2373 Contig46937_RC SEQ ID NO 2377 Contig48004_RC SEQ ID NO 2393 Contig48249_RC SEQ ID NO 2397 Contig48774_RC SEQ ID NO 2405 Contig48913_RC SEQ ID NO 2411 Contig48945_RC SEQ ID NO 2412 Contig48970_RC SEQ ID NO 2413 Contig49233_RC SEQ ID NO 2419 Contig49289_RC SEQ ID NO 2422 Contig49342_RC SEQ ID NO 2423 Contig49510_RC SEQ ID NO 2430 Contig49855 SEQ ID NO 2440 Contig49948_RC SEQ ID NO 2442 Contig50297_RC SEQ ID NO 2451 Contig50669_RC SEQ ID NO 2458 Contig50673_RC SEQ ID NO 2459 Contig50838_RC SEQ ID NO 2465 Contig51068_RC SEQ ID NO 2471 Contig51929 SEQ ID NO 2492 Contig51953_RC SEQ ID NO 2493 Contig52405_RC SEQ ID NO 2502 Contig52543_RC SEQ ID NO 2505 Contig52720_RC SEQ ID NO 2513 Contig53281_RC SEQ ID NO 2530 Contig53598_RC SEQ ID NO 2537 Contig53757_RC SEQ ID NO 2543 Contig53944_RC SEQ ID NO 2545 Contig54425 SEQ ID NO 2561 Contig54547_RC SEQ ID NO 2565 Contig54757_RC SEQ ID NO 2574 Contig54916_RC SEQ ID NO 2581 Contig55770_RC SEQ ID NO 2604 Contig55801_RC SEQ ID NO 2606 Contig56143_RC SEQ ID NO 2619 Contig56160_RC SEQ ID NO 2620 Contig56303_RC SEQ ID NO 2626 Contig57023_RC SEQ ID NO 2639 Contig57138_RC SEQ ID NO 2644 Contig57609_RC SEQ ID NO 2657 Contig58301_RC SEQ ID NO 2667 Contig58512_RC SEQ ID NO 2670 Contig60393 SEQ ID NO 2674 Contig60509_RC SEQ ID NO 2675 Contig61254_RC SEQ ID NO 2677 Contig62306 SEQ ID NO 2680 Contig64502 SEQ ID NO 2689

[0089] TABLE 4 100 preferred markers from Table 3 distinguishing BRCA1-related tumors from sporadic tumors. Sequence Identifier Correlation Name Description NM_001892 −0.651689 CSNK1A1 casein kinase 1, alpha 1 NM_018171 −0.637696 FLJ10659 hypothetical protein FLJ10659 Contig40712_RC −0.612509 ESTs NM_001204 −0.608470 BMPR2 bone morphogenetic protein receptor, type II (serine/threonine kinase) NM_005148 −0.598612 UNC119 unc119 (C. elegans) homolog G26403 0.585054 YWHAH tyrosine 3- monooxygenase/tryptophan 5- monooxygenase activation protein, eta polypeptide NM_015640 0.583397 PAI-RBP1 PAI-1 mRNA-binding protein Contig9259_RC 0.581362 ESTs AB033049 −0.578750 KIAA1223 KIAA1223 protein NM_015523 0.576029 DKFZP566E144 small fragment nuclease Contig41402_RC −0.571650 Human DNA sequence from clone RP11-16L21 on chromosome 9. Contains the gene for NADP- dependent leukotriene B4 12- hydroxydehydrogenase, the gene for a novel DnaJ domain protein similar to Drosophila, C. elegans and Arabidopsis predicted proteins, the GNG10 gene for guanine nucleotide binding protein 10, a novel gene, ESTs, STSs, GSSs and six CpG islands NM_004791 −0.564819 ITGBL1 integrin, beta-like 1 (with EGF-like repeat domains) NM_007070 0.561173 FAP48 FKBP-associated protein NM_014597 0.555907 HSU15552 acidic 82 kDa protein mRNA AF000974 0.547194 TRIP6 thyroid hormone receptor interactor 6 NM_016073 −0.547072 CGI-142 CGI-142 Contig3940_RC 0.544073 YWHAH tyrosine 3- monooxygenase/tryptophan 5- monooxygenase activation protein, eta polypeptide NM_003683 0.542219 D21S2056E DNA segment on chromosome 21 (unique) 2056 expressed sequence Contig58512_RC −0.528458 Homo sapiens pancreas tumor- related protein (FKSG12) mRNA, complete cds NM_003904 0.521223 ZNF259 zinc finger protein 259 Contig26022_RC 0.517351 ESTs Contig48970_RC −0.516953 KIAA0892 KIAA0892 protein NM_016307 −0.515398 PRX2 paired related homeobox protein AL137761 −0.514891 Homo sapiens mRNA; cDNA DKFZp586L2424 (from clone DKFZp586L2424) NM_001919 −0.514799 DCI dodecenoyl-Coenzyme A delta isomerase (3,2 trans-enoyl- Coenzyme A isomerase) NM_000196 −0.514004 HSD11B2 hydroxysteroid (11-beta) dehydrogenase 2 NM_002200 0.513149 IRF5 interferon regulatory factor 5 AL133572 0.511340 Homo sapiens mRNA; cDNA DKFZp434I0535 (from clone DKFZp434I0535); partial cds NM_019063 0.511127 C2ORF2 chromosome 2 open reading frame 2 Contig25617_RC 0.509506 ESTs NM_007358 0.508145 M96 putative DNA binding protein NM_014785 −0.507114 KIAA0258 KIAA0258 gene product NM_006235 0.506585 POU2AF1 POU domain, class 2, associating factor 1 NM_014680 −0.505779 KIAA0100 KIAA0100 gene product X66087 0.500842 MYBL1 v-myb avian myeloblastosis viral oncogene homolog-like 1 Y07512 −0.500686 PRKG1 protein kinase, cGMP-dependent, type I NM_006296 0.500344 VRK2 vaccinia related kinase 2 Contig44278_RC 0.498260 DKFZP434K114 DKFZP434K114 protein Contig56160_RC −0.497695 ESTs NM_002023 −0.497570 FMOD fibromodulin M28170 0.497095 CD19 CD19 antigen D26488 0.496511 KIAA0007 KIAA0007 protein X72475 0.496125 H. sapiens mRNA for rearranged Ig kappa light chain variable region (I.114) K02276 0.496068 MYC v-myc avian myelocytomatosis viral oncogene homolog NM_013378 0.495648 VPREB3 pre-B lymphocyte gene 3 X58529 0.495608 IGHM immunoglobulin heavy constant mu NM_000168 −0.494260 GLI3 GLI-Kruppel family member GLI3 (Greig cephalopolysyndactyly syndrome) NM_004866 −0.492967 SCAMP1 secretory carrier membrane protein 1 NM_013253 −0.491159 DKK3 dickkopf (Xenopus laevis) homolog 3 NM_003729 0.488971 RPC RNA 3′-terminal phosphate cyclase NM_006875 0.487407 PIM2 pim-2 oncogene NM_018188 0.487126 FLJ10709 hypothetical protein FLJ10709 NM_004848 0.485408 ICB-1 basement membrane-induced gene NM_001179 0.483253 ART3 ADP-ribosyltransferase 3 NM_016548 −0.482329 LOC51280 golgi membrane protein GP73 NM_007146 −0.481994 ZNF161 zinc finger protein 161 NM_021242 −0.481754 STRAIT11499 hypothetical protein STRAITI1499 NM_016223 0.481710 PACSIN3 protein kinase C and casein kinase substrate in neurons 3 NM_003197 −0.481526 TCEB1L transcription elongation factor B (SIII), polypeptide 1-like NM_000067 −0.481003 CA2 carbonic anhydrase II NM_006885 −0.479705 ATBF1 AT-binding transcription factor 1 NM_002542 0.478282 OGG1 8-oxoguanine DNA glycosylase AL133619 −0.476596 Homo sapiens mRNA; cDNA DKFZp434E2321 (from clone DKFZp434E2321); partial cds D80001 0.476130 KIAA0179 KIAA0179 protein NM_018660 −0.475548 LOC55893 papillomavirus regulatory factor PRF-1 AB004857 0.473440 SLC11A2 solute carrier family 11 (proton- coupled divalent metal ion transporters), member 2 NM_002250 0.472900 KCNN4 potassium intermediate/small conductance calcium-activated channel, subfamily N, member 4 Contig56143_RC −0.472611 ESTs, Weakly similar to A54849 collagen alpha 1(VII) chain precursor [H. sapiens] NM_001960 0.471502 EEF1D eukaryotic translation elongation factor 1 delta (guanine nucleotide exchange protein) Contig52405_RC −0.470705 ESTs, Weakly similar to ALU8_HUMAN ALU SUBFAMILY SX SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Contig30092_RC −0.469977 Homo sapiens PR-domain zinc finger protein 6 isoform B (PRDM6) mRNA, partial cds; alternatively spliced NM_003462 −0.468753 P28 dynein, axonemal, light intermediate polypeptide Contig60393 0.468475 ESTs Contig842_RC 0.468158 ESTs NM_002982 0.466362 SCYA2 small inducible cytokine A2 (monocyte chemotactic protein 1, homologous to mouse Sig-je) Contig14390_RC 0.464150 ESTs NM_001770 0.463847 CD19 CD19 antigen AK000617 −0.463158 Homo sapiens mRNA; cDNA DKFZp434L235 (from clone DKFZp434L235) AF073299 −0.463007 SLC9A2 solute carrier family 9 (sodium/hydrogen exchanger), isoform 2 NM_019049 0.461990 FLJ20054 hypothetical protein AL137347 −0.460778 DKFZP761M1511 hypothetical protein NM_000396 −0.460263 CTSK cathepsin K (pycnodysostosis) NM_018373 −0.459268 FLJ11271 hypothetical protein FLJ11271 NM_002709 0.458500 PPP1CB protein phosphatase 1, catalytic subunit, beta isoform NM_016820 0.457516 OGG1 8-oxoguanine DNA glycosylase Contig10268_RC 0.456933 Human DNA sequence from clone RP11-196N14 on chromosome 20 Contains ESTs, STSs, GSSs and CpG islands. Contains three novel genes, part of a gene for a novel protein similar to protein serine/threonine phosphatase 4 regulatory subunit 1 (PP4R1) and a gene for a novel protein with an ankyrin domain NM_014521 −0.456733 SH3BP4 SH3-domain binding protein 4 AJ272057 −0.456548 STRAIT11499 hypothetical protein STRAIT11499 NM_015964 −0.456187 LOC51673 brain specific protein Contig16759_RC −0.456169 ESTs NM_015937 −0.455954 LOC51604 CGI-06 protein NM_007246 −0.455500 KLHL2 kelch (Drosophila)-like 2 (Mayven) NM_001985 −0.453024 ETFB electron-transfer-flavoprotein, beta polypeptide NM_000984 −0.452935 RPL23A ribosomal protein L23a Contig51953_RC −0.451695 ESTs NM_015984 0.450491 UCH37 ubiquitin C-terminal hydrolase UCH37 NM_000903 −0.450371 DIA4 diaphorase (NADH/NADPH) (cytochrome b-5 reductase) NM_001797 −0.449862 CDH11 cadherin 11, type 2, OB-cadherin (osteoblast) NM_014878 0.449818 KIAA0020 KIAA0020 gene product NM_002742 −0.449590 PRKCM protein kinase C, mu

[0090] TABLE 5 231 gene markers that distinguish patients with good prognosis from patients with poor prognosis. GenBank Accession Number SEQ ID NO AA555029_RC SEQ ID NO 1 AB020689 SEQ ID NO 37 AB032973 SEQ ID NO 55 AB033007 SEQ ID NO 58 AB033043 SEQ ID NO 62 AB037745 SEQ ID NO 75 AB037863 SEQ ID NO 88 AF052159 SEQ ID NO 120 AF052162 SEQ ID NO 121 AF055033 SEQ ID NO 124 AF073519 SEQ ID NO 137 AF148505 SEQ ID NO 169 AF155117 SEQ ID NO 173 AF161553 SEQ ID NO 177 AF201951 SEQ ID NO 183 AF257175 SEQ ID NO 189 AJ224741 SEQ ID NO 196 AK000745 SEQ ID NO 219 AL050021 SEQ ID NO 257 AL050090 SEQ ID NO 259 AL080059 SEQ ID NO 270 AL080079 SEQ ID NO 271 AL080110 SEQ ID NO 272 AL133603 SEQ ID NO 306 AL133619 SEQ ID NO 307 AL137295 SEQ ID NO 315 AL137502 SEQ ID NO 326 AL137514 SEQ ID NO 327 AL137718 SEQ ID NO 336 AL355708 SEQ ID NO 353 D25328 SEQ ID NO 357 L27560 SEQ ID NO 390 M21551 SEQ ID NO 394 NM_000017 SEQ ID NO 416 NM_000096 SEQ ID NO 430 NM_000127 SEQ ID NO 436 NM_000158 SEQ ID NO 442 NM_000224 SEQ ID NO 453 NM_000286 SEQ ID NO 462 NM_000291 SEQ ID NO 463 NM_000320 SEQ ID NO 469 NM_000436 SEQ ID NO 487 NM_000507 SEQ ID NO 491 NM_000599 SEQ ID NO 503 NM_000788 SEQ ID NO 527 NM_000849 SEQ ID NO 530 NM_001007 SEQ ID NO 550 NM_001124 SEQ ID NO 562 NM_001168 SEQ ID NO 566 NM_001216 SEQ ID NO 574 NM_001280 SEQ ID NO 588 NM_001282 SEQ ID NO 589 NM_001333 SEQ ID NO 597 NM_001673 SEQ ID NO 645 NM_001809 SEQ ID NO 673 NM_001827 SEQ ID NO 676 NM_001905 SEQ ID NO 691 NM_002019 SEQ ID NO 711 NM_002073 SEQ ID NO 721 NM_002358 SEQ ID NO 764 NM_002570 SEQ ID NO 787 NM_002808 SEQ ID NO 822 NM_002811 SEQ ID NO 823 NM_002900 SEQ ID NO 835 NM_002916 SEQ ID NO 838 NM_003158 SEQ ID NO 881 NM_003234 SEQ ID NO 891 NM_003239 SEQ ID NO 893 NM_003258 SEQ ID NO 896 NM_003376 SEQ ID NO 906 NM_003600 SEQ ID NO 929 NM_003607 SEQ ID NO 930 NM_003662 SEQ ID NO 938 NM_003676 SEQ ID NO 941 NM_003748 SEQ ID NO 951 NM_003862 SEQ ID NO 960 NM_003875 SEQ ID NO 962 NM_003878 SEQ ID NO 963 NM_003882 SEQ ID NO 964 NM_003981 SEQ ID NO 977 NM_004052 SEQ ID NO 985 NM_004163 SEQ ID NO 995 NM_004336 SEQ ID NO 1022 NM_004358 SEQ ID NO 1026 NM_004456 SEQ ID NO 1043 NM_004480 SEQ ID NO 1046 NM_004504 SEQ ID NO 1051 NM_004603 SEQ ID NO 1064 NM_004701 SEQ ID NO 1075 NM_004702 SEQ ID NO 1076 NM_004798 SEQ ID NO 1087 NM_004911 SEQ ID NO 1102 NM_004994 SEQ ID NO 1108 NM_005196 SEQ ID NO 1127 NM_005342 SEQ ID NO 1143 NM_005496 SEQ ID NO 1157 NM_005563 SEQ ID NO 1173 NM_005915 SEQ ID NO 1215 NM_006096 SEQ ID NO 1240 NM_006101 SEQ ID NO 1241 NM_006115 SEQ ID NO 1245 NM_006117 SEQ ID NO 1246 NM_006201 SEQ ID NO 1254 NM_006265 SEQ ID NO 1260 NM_006281 SEQ ID NO 1263 NM_006372 SEQ ID NO 1273 NM_006681 SEQ ID NO 1306 NM_006763 SEQ ID NO 1315 NM_006931 SEQ ID NO 1341 NM_007036 SEQ ID NO 1349 NM_007203 SEQ ID NO 1362 NM_012177 SEQ ID NO 1390 NM_012214 SEQ ID NO 1392 NM_012261 SEQ ID NO 1397 NM_012429 SEQ ID NO 1413 NM_013262 SEQ ID NO 1425 NM_013296 SEQ ID NO 1427 NM_013437 SEQ ID NO 1439 NM_014078 SEQ ID NO 1449 NM_014109 SEQ ID NO 1451 NM_014321 SEQ ID NO 1477 NM_014363 SEQ ID NO 1480 NM_014750 SEQ ID NO 1527 NM_014754 SEQ ID NO 1528 NM_014791 SEQ ID NO 1535 NM_014875 SEQ ID NO 1545 NM_014889 SEQ ID NO 1548 NM_014968 SEQ ID NO 1554 NM_015416 SEQ ID NO 1559 NM_015417 SEQ ID NO 1560 NM_015434 SEQ ID NO 1562 NM_015984 SEQ ID NO 1587 NM_016337 SEQ ID NO 1636 NM_016359 SEQ ID NO 1638 NM_016448 SEQ ID NO 1645 NM_016569 SEQ ID NO 1655 NM_016577 SEQ ID NO 1656 NM_017779 SEQ ID NO 1708 NM_018004 SEQ ID NO 1725 NM_018098 SEQ ID NO 1739 NM_018104 SEQ ID NO 1743 NM_018120 SEQ ID NO 1745 NM_018136 SEQ ID NO 1748 NM_018265 SEQ ID NO 1766 NM_018354 SEQ ID NO 1774 NM_018401 SEQ ID NO 1782 NM_018410 SEQ ID NO 1783 NM_018454 SEQ ID NO 1786 NM_018455 SEQ ID NO 1787 NM_019013 SEQ ID NO 18O9 NM_020166 SEQ ID NO 1825 NM_020188 SEQ ID NO 1830 NM_020244 SEQ ID NO 1835 NM_020386 SEQ ID NO 1838 NM_020675 SEQ ID NO 1842 NM_020974 SEQ ID NO 1844 R70506_RC SEQ ID NO 1868 U45975 SEQ ID NO 1878 U58033 SEQ ID NO 1881 U82987 SEQ ID NO 1891 U96131 SEQ ID NO 1896 X05610 SEQ ID NO 1903 X94232 SEQ ID NO 1927 Contig753_RC SEQ ID NO 1954 Contig1778_RC SEQ ID NO 1979 Contig2399_RC SEQ ID NO 1989 Contig2504_RC SEQ ID NO 1991 Contig3902_RC SEQ ID NO 2017 Contig4595 SEQ ID NO 2022 Contig8581_RC SEQ ID NO 2037 Contig13480_RC SEQ ID NO 2052 Contig17359_RC SEQ ID NO 2068 Contig20217_RC SEQ ID NO 2072 Contig21812_RC SEQ ID NO 2082 Contig24252_RC SEQ ID NO 2087 Contig25055_RC SEQ ID NO 2090 Contig25343_RC SEQ ID NO 2092 Contig25991 SEQ ID NO 2098 Contig27312_RC SEQ ID NO 2108 Contig28552_RC SEQ ID NO 2120 Contig32125_RC SEQ ID NO 2155 Contig32185_RC SEQ ID NO 2156 Contig33814_RC SEQ ID NO 2169 Contig34634_RC SEQ ID NO 2180 Contig35251_RC SEQ ID NO 2185 Contig37063_RC SEQ ID NO 2206 Contig37598 SEQ ID NO 2216 Contig38288_RC SEQ ID NO 2223 Contig40128_RC SEQ ID NO 2248 Contig40831_RC SEQ ID NO 2260 Contig41413_RC SEQ ID NO 2266 Contig41887_RC SEQ ID NO 2276 Contig42421_RC SEQ ID NO 2291 Contig43747_RC SEQ ID NO 2311 Contig44064_RC SEQ ID NO 2315 Contig44289_RC SEQ ID NO 2320 Contig44799_RC SEQ ID NO 2330 Contig45347_RC SEQ ID NO 2344 Contig45816_RC SEQ ID NO 2351 Contig46218_RC SEQ ID NO 2358 Contig46223_RC SEQ ID NO 2359 Contig46653_RC SEQ ID NO 2369 Contig46802_RC SEQ ID NO 2372 Contig47405_RC SEQ ID NO 2384 Contig48328_RC SEQ ID NO 2400 Contig49670_RC SEQ ID NO 2434 Contig50106_RC SEQ ID NO 2445 Contig50410 SEQ ID NO 2453 Contig50802_RC SEQ ID NO 2463 Contig51464_RC SEQ ID NO 2481 Contig51519_RC SEQ ID NO 2482 Contig51749_RC SEQ ID NO 2486 Contig51963 SEQ ID NO 2494 Contig53226_RC SEQ ID NO 2525 Contig53268_RC SEQ ID NO 2529 Contig53646_RC SEQ ID NO 2538 Contig53742_RC SEQ ID NO 2542 Contig55188_RC SEQ ID NO 2586 Contig55313_RC SEQ ID NO 2590 Contig55377_RC SEQ ID NO 2591 Contig55725_RC SEQ ID NO 2600 Contig55813_RC SEQ ID NO 2607 Contig55829_RC SEQ ID NO 2608 Contig56457_RC SEQ ID NO 2630 Contig57595 SEQ ID NO 2655 Contig57864_RC SEQ ID NO 2663 Contig58368_RC SEQ ID NO 2668 Contig60864_RC SEQ ID NO 2676 Contig63102_RC SEQ ID NO 2684 Contig63649_RC SEQ ID NO 2686 Contig64688 SEQ ID NO 2690

[0091] TABLE 6 70 Preferred prognosis markers drawn from Table 5. Sequence Identifier Correlation Name Description AL080059 −0.527150 Homo sapiens mRNA for KIAA1750 protein, partial cds Contig63649_(—) −0.468130 ESTs RC Contig46218_(—) −0.432540 ESTs RC NM_016359 −0.424930 LOC51203 clone HQ0310 PRO0310p1 AA555029_RC −0.424120 ESTs NM_003748 0.420671 ALDH4 aldehyde dehydrogenase 4 (glutamate gamma-semialdehyde dehydrogenase; pyrroline-5- carboxylate dehydrogenase) Contig38288_(—) −0.414970 ESTs, Weakly similar to ISHUSS RC protein disulfide-isomerase [H. sapiens ] NM_003862 0.410964 FGF18 fibroblast growth factor 18 Contig28552_(—) −0.409260 Homo sapiens mRNA; cDNA RC DKFZp434C0931 (from clone DKFZp434C0931); partial cds Contig32125_(—) 0.409054 ESTs RC U82987 0.407002 BBC3 Bcl-2 binding component 3 AL137718 −0.404980 Homo sapiens mRNA; cDNA DKFZp434C0931 (from clone DKFZp434C0931); partial cds AB037863 0.402335 KIAA1442 KIAA1442 protein NM_020188 −0.400070 DC13 DC13 protein NM_020974 0.399987 CEGP1 CEGP1 protein NM_000127 −0.399520 EXT1 exostoses (multiple) 1 NM_002019 −0.398070 FLT1 fms-related tyrosine kinase 1 (vascular endothelial growth factor/vascular permeability factor receptor) NM_002073 −0.395460 GNAZ guanine nucleotide binding protein (G protein), alpha z polypeptide NM_000436 −0.392120 OXCT 3-oxoacid CoA transferase NM_004994 −0.391690 MMP9 matrix metalloproteinase 9 (gelatinase B, 92 kD gelatinase, 92 kD type IV collagenase) Contig55377_(—) 0.390600 ESTs RC Contig35251_(—) −0.390410 Homo sapiens cDNA: FLJ22719 fis, RC clone HSI14307 Contig25991 −0.390370 ECT2 epithelial cell transforming sequence 2 oncogene NM_003875 −0.386520 GMPS guanine monphosphate synthetase NM_006101 −0.385890 HEC highly expressed in cancer, rich in leucine heptad repeats NM_003882 0.384479 WISP1 WNT1 inducible signaling pathway protein 1 NM_003607 −0.384390 PK428 Ser-Thr protein kinase related to the myotonic dystrophy protein kinase AF073519 −0.383340 SERF1A small EDRK-rich factor 1A (telomeric) AF052162 −0.380830 FLJ12443 hypothetical protein FLJ12443 NM_000849 0.380831 GSTM3 glutathione S-transferase M3 (brain) Contig32185_(—) −0.379170 Homo sapiens cDNA FLJ13997 fis, RC clone Y79AA1002220 NM_016577 −0.376230 RAB6B RAB6B, member RAS oncogene family Contig48328_(—) 0.375252 ESTs, Weakly similar to T17248 RC hypothetical protein DKFZp586G1122.1 [H. sapiens ] Contig46223_(—) 0.374289 ESTs RC NM_015984 −0.373880 UCH37 ubiquitin C-terminal hydrolase UCH37 NM_006117 0.373290 PECI peroxisomal D3,D2-enoyl-CoA isomerase AK000745 −0.373060 Homo sapiens cDNA FLJ20738 fis, clone HEP08257 Contig40831_(—) −0.372930 ESTs RC NM_003239 0.371524 TGFB3 transforming growth factor, beta 3 NM_014791 −0.370860 KIAA0175 KIAA0175 gene product X05610 −0.370860 COL4A2 collagen, type IV, alpha 2 NM_016448 −0.369420 L2DTL L2DTL protein NM_018401 0.368349 HSA250839 gene for serine/threonine protein kinase NM_000788 −0.367700 DCK deoxycytidine kinase Contig51464_(—) −0.367450 FLJ22477 hypothetical protein FLJ22477 RC AL080079 −0.367390 DKFZP564D0462 hypothetical protein DKFZp564D0462 NM_006931 −0.366490 SLC2A3 solute carrier family 2 (facilitated glucose transporter), member 3 AF257175 0.365900 Homo sapiens hepatocellular carcinoma-associated antigen 64 (HCA64) mRNA, complete cds NM_014321 −0.365810 ORC6L origin recognition complex, subunit 6 (yeast homolog)-like NM_002916 −0.365590 RFC4 replication factor C (activator 1) 4 (37 kD) Contig55725_(—) −0.365350 ESTs, Moderately similar to T50635 RC hypothetical protein DKFZp762L0311.1 [H. sapiens ] Contig24252_(—) −0.364990 ESTs RC AF201951 0.363953 CFFM4 high affinity immunoglobulin epsilon receptor beta subunit NM_005915 −0.363850 MCM6 minichromosome maintenance deficient (mis5, S. pombe) 6 NM_001282 0.363326 AP2B1 adaptor-related protein complex 2, beta 1 subunit Contig56457_(—) −0.361650 TMEFF1 transmembrane protein with EGF- RC like and two follistatin-like domains 1 NM_000599 −0.361290 IGFBP5 insulin-like growth factor binding protein 5 NM_020386 −0.360780 LOC57110 H-REV107 protein-related protein NM_014889 −0.360040 MP1 metalloprotease 1 (pitrilysin family) AF055033 −0.359940 IGFBP5 insulin-like growth factor binding protein 5 NM_006681 −0.359700 NMU neuromedin U NM_007203 −0.359570 AKAP2 A kinase (PRKA) anchor protein 2 Contig63102_(—) 0.359255 FLJ11354 hypothetical protein FLJ11354 RC NM_003981 −0.358260 PRC1 protein regulator of cytokinesis 1 Contig20217_(—) −0.357880 ESTs RC NM_001809 −0.357720 CENPA centromere protein A (17 kD) Contig2399_RC −0.356600 SM-20 similar to rat smooth muscle protein SM-20 NM_004702 −0.356600 CCNE2 cyclin E2 NM_007036 −0.356540 ESM1 endothelial cell-specific molecule 1 NM_018354 −0.356000 FLJ11190 hypothetical protein FLJ11190

[0092] TABLE 7 Good and poor prognosis templates: mean subtracted log(intensity) values for each of the seventy markers listed in Table 6 for 44 breast cancer patients having a good prognosis (C1) or 34 breast cancer patients having a poor prognosis (C2) (see Examples). Marker C1 C2 Accession # (good prognosis template) (poor prognosis template) AL080059 −5.161569 0.043019 Contig63649_RC −1.440895 0.966702 Contig46218_RC −0.937662 0.815081 NM_016359 −1.49878 0.872829 AA555029_RC −1.283504 0.543442 NM_003748 1.355486 −0.254201 Contig38288_RC −1.237495 1.085461 NM_003862 0.981236 −1.619658 Contig28552_RC −1.296043 1.067545 Contig32125_RC 0.855155 −0.7338 U82987 1.256206 −1.362807 AL137718 −0.55046 0.68754 AB037863 0.819061 −1.621057 NM_020188 −1.137582 0.673123 NM_020974 −0.463953 −5.623268 NM_000127 −0.618568 0.552726 NM_002019 −1.409168 0.547285 NM_002073 −1.577177 0.417352 NM_000436 −0.722574 0.599239 NM_004994 −5.561089 −2.180659 Contig55377_RC 0.805683 −1.16728 Contig35251_RC −0.931146 0.607562 Contig25991 −0.720727 1.045949 NM_003875 −1.206839 1.163244 NM_006101 −0.879965 0.628296 NM_003882 0.529121 −0.467098 NM_003607 −0.959094 0.709653 AF073519 −1.451486 0.163988 AF052162 −1.145575 0.192391 NM_000849 0.944742 −1.499473 Contig32185_RC −0.887643 0.688257 NM_016577 −2.762008 0.081637 Contig48328_RC 0.405401 −2.946904 Contig46223_RC 0.805424 −0.581849 NM_015984 −1.056531 0.522176 NM_006117 1.129928 −1.262974 AK000745 −2.475715 −0.013002 Contig40831_RC −1.17091 0.435754 NM_003239 0.457773 −2.150499 NM_014791 −1.14862 0.383018 X05610 −0.768514 0.637938 NM_016448 −0.713264 0.632638 NM_018401 0.618921 −0.286778 NM_000788 −0.995116 0.50246 Contig51464_RC −0.663538 0.765975 AL080079 −1.794821 0.43708 NM_006931 −0.846271 0.915602 AF257175 1.122354 −0.721924 NM_014321 −1.820261 0.482287 NM_002916 −0.966852 0.599925 Contig55725_RC −2.935162 0.623397 Contig24252_RC −2.004671 0.263597 AF201951 0.355839 −2.296556 NM_005915 −0.586121 0.827714 NM_001282 0.762645 −0.970418 Contig56457_RC −0.920808 0.588269 NM_000599 −3.612469 −0.714313 NM_020386 −0.46073 0.699313 NM_014889 −1.678462 0.2362 AF055033 −2.505271 −0.07576 NM_006681 −0.631302 0.584119 NM_007203 −1.426446 0.504624 Contig63102_RC 0.521511 −1.266163 NM_003981 −2.521877 0.552669 Contig20217_RC −0.363574 0.449022 NM_001809 −2.171301 0.328419 Contig2399_RC −1.174844 0.602523 NM_004702 −1.560133 0.619078 NM_007036 −0.950633 0.34945 NM_018354 −1.392354 0.347831

[0093] The sets of markers listed in Tables 1-6 partially overlap; in other words, some markers are present in multiple sets, while other markers are unique to a set (FIG. 1). Thus, in one embodiment, the invention provides a set of 256 genetic markers that can distinguish between ER(+) and ER(−), and also between BRCA1 tumors and sporadic tumors (i.e., classify a tumor as ER(−) or ER(−) and BRCA1-related or sporadic). In a more specific embodiment, the invention provides subsets of at least 20, at least 50, at least 100, or at least 150 of the set of 256 markers, that can classify a tumor as ER(−) or ER(−) and BRCA1-related or sporadic. In another embodiment, the invention provides 165 markers that can distinguish between ER(+) and ER(−), and also between patients with good versus poor prognosis (i.e., classify a tumor as either ER(−) or ER(+) and as having been removed from a patient with a good prognosis or a poor prognosis). In a more specific embodiment, the invention further provides subsets of at least 20, 50, 100 or 125 of the full set of 165 markers, which also classify a tumor as either ER(−) or ER(+) and as having been removed from a patient with a good prognosis or a poor prognosis The invention further provides a set of twelve markers that can distinguish between BRCA1 tumors and sporadic tumors, and between patients with good versus poor prognosis. Finally, the invention provides eleven markers capable of differentiating all three statuses. Conversely, the invention provides 2,050 of the 2,460 ER-status markers that can determine only ER status, 173 of the 430 BRCA1 v. sporadic markers that can determine only BRCA1 v. sporadic status, and 65 of the 231 prognosis markers that can only determine prognosis. In more specific embodiments, the invention also provides for subsets of at least 20, 50, 100, 200, 500, 1,000, 1,500 or 2,000 of the 2,050 ER-status markers that also determine only ER status. The invention also provides subsets of at least 20, 50, 100 or 150 of the 173 markers that also determine only BRCA1 v. sporadic status. The invention further provides subsets of at least 20, 30, 40, or 50 of the 65 prognostic markers that also determine only prognostic status.

[0094] Any of the sets of markers provided above may be used alone specifically or in combination with markers outside the set. For example, markers that distinguish ER-status may be used in combination with the BRCA1 vs. sporadic markers, or with the prognostic markers, or both. Any of the marker sets provided above may also be used in combination with other markers for breast cancer, or for any other clinical or physiological condition.

[0095] The relationship between the marker sets is diagramed in FIG. 1.

5.3.2 Identification of Markers

[0096] The present invention provides sets of markers for the identification of conditions or indications associated with breast cancer. Generally, the marker sets were identified by determining which of ˜25,000 human markers had expression patters that correlated with the conditions or indications.

[0097] In one embodiment, the method for identifying marker sets is as follows. After extraction and labeling of target polynucleotides, the expression of all markers (genes) in a sample X is compared to the expression of all markers in a standard or control. In one embodiment, the standard or control comprises target polynucleotide molecules derived from a sample from a normal individual (i.e., an individual not afflicted with breast cancer). In a preferred embodiment, the standard or control is a pool of target polynucleotide molecules. The pool may derived from collected samples from a number of normal individuals. In a preferred embodiment, the pool comprises samples taken from a number of individuals having sporadic-type tumors. In another preferred embodiment, the pool comprises an artificially-generated population of nucleic acids designed to approximate the level of nucleic acid derived from each marker found in a pool of marker-derived nucleic acids derived from tumor samples. In yet another embodiment, the pool is derived from normal or breast cancer cell lines or cell line samples.

[0098] The comparison may be accomplished by any means known in the art. For example, expression levels of various markers may be assessed by separation of target polynucleotide molecules (e.g., RNA or cDNA) derived from the markers in agarose or polyacrylamide gels, followed by hybridization with marker-specific oligonucleotide probes. Alternatively, the comparison may be accomplished by the labeling of target polynucleotide molecules followed by separation on a sequencing gel. Polynucleotide samples are placed on the gel such that patient and control or standard polynucleotides are in adjacent lanes. Comparison of expression levels is accomplished visually or by means of densitometer. In a preferred embodiment, the expression of all markers is assessed simultaneously by hybridization to a microarray. In each approach, markers meeting certain criteria are identified as associated with breast cancer.

[0099] A marker is selected based upon significant difference of expression in a sample as compared to a standard or control condition. Selection may be made based upon either significant up- or down regulation of the marker in the patient sample. Selection may also be made by calculation of the statistical significance (i.e., the p-value) of the correlation between the expression of the marker and the condition or indication. Preferably, both selection criteria are used. Thus, in one embodiment of the present invention, markers associated with breast cancer are selected where the markers show both more than two-fold change (increase or decrease) in expression as compared to a standard, and the p-value for the correlation between the existence of breast cancer and the change in marker expression is no more than 0.01 (i.e., is statistically significant).

[0100] The expression of the identified breast cancer-related markers is then used to identify markers that can differentiate tumors into clinical types. In a specific embodiment using a number of tumor samples, markers are identified by calculation of correlation coefficients between the clinical category or clinical parameter(s) and the linear, logarithmic or any transform of the expression ratio across all samples for each individual gene. Specifically, the correlation coefficient is calculated as

ρ=({right arrow over (c)}•{right arrow over (r)})/(∥{right arrow over (c)}∥·∥{right arrow over (r)}∥)  Equation (2)

[0101] where {right arrow over (c)} represents the clinical parameters or categories and {right arrow over (r)} represents the linear, logarithmic or any transform of the ratio of expression between sample and control. Markers for which the coefficient of correlation exceeds a cutoff are identified as breast cancer-related markers specific for a particular clinical type. Such a cutoff or threshold corresponds to a certain significance of discriminating genes obtained by Monte Carlo simulations. The threshold depends upon the number of samples used; the threshold can be calculated as 3×1/{square root}{square root over (n−3)}, where 1/{square root}{square root over (n−3)} is the distribution width and n=the number of samples. In a specific embodiment, markers are chosen if the correlation coefficient is greater than about 0.3 or less than about −0.3.

[0102] Next, the significance of the correlation is calculated. This significance may be calculated by any statistical means by which such significance is calculated. In a specific example, a set of correlation data is generated using a Monte-Carlo technique to randomize the association between the expression difference of a particular marker and the clinical category. The frequency distribution of markers satisfying the criteria through calculation of correlation coefficients is compared to the number of markers satisfying the criteria in the data generated through the Monte-Carlo technique. The frequency distribution of markers satisfying the criteria in the Monte-Carlo runs is used to determine whether the number of markers selected by correlation with clinical data is significant. See Example 4.

[0103] Once a marker set is identified, the markers may be rank-ordered in order of significance of discrimination. One means of rank ordering is by the amplitude of correlation between the change in gene expression of the marker and the specific condition being discriminated. Another, preferred, means is to use a statistical metric. In a specific embodiment, the metric is a Fisher-like statistic:

t=(

x ₁

−

x ₂

)/{square root}{square root over ([σ₁ ²(n ₁−1)+σ₂ ²(n ₂−1)]/(n ₁ +n ₂−1)/(1/n ₁+1/n ₂))}  Equation (3)

[0104] In this equation,

x₁

is the error-weighted average of the log ratio of transcript expression measurements within a first diagnostic group (e.g., ER(−),

x₂

is the error-weighted average of log ratio within a second, related diagnostic group (e.g., ER(+)), σ₁ is the variance of the log ratio within the ER(−) group and n₁ is the number of samples for which valid measurements of log ratios are available. σ₂ is the variance of log ratio within the second diagnostic group (e.g., ER(+)), and n₂ is the number of samples for which valid measurements of log ratios are available. The t-value represents the variance-compensated difference between two means.

[0105] The rank-ordered marker set may be used to optimize the number of markers in the set used for discrimination. This is accomplished generally in a “leave one out” method as follows. In a first run, a subset, for example 5, of the markers from the top of the ranked list is used to generate a template, where out of X samples, X-1 are used to generate the template, and the status of the remaining sample is predicted. This process is repeated for every sample until every one of the X samples is predicted once. In a second run, additional markers, for example 5, are added, so that a template is now generated from 10 markers, and the outcome of the remaining sample is predicted. This process is repeated until the entire set of markers is used to generate the template. For each of the runs, type 1 error (false negative) and type 2 errors (false positive) are counted; the optimal number of markers is that number where the type 1 error rate, or type 2 error rate, or preferably the total of type 1 and type 2 error rate is lowest.

[0106] For prognostic markers, validation of the marker set may be accomplished by an additional statistic, a survival model. This statistic generates the probability of tumor distant metastases as a function of time since initial diagnosis. A number of models may be used, including Weibull, normal, log-normal, log logistic, log-exponential, or log-Rayleigh (Chapter 12 “Life Testing”, S-PLUS 2000 GUIDE TO STATISTICS, Vol. 2, p. 368 (2000)). For the “normal” model, the probability of distant metastases P at time t is calculated as

P=α×exp(−t ²/τ²)  Equation (4)

[0107] where α is fixed and equal to 1, and τ is a parameter to be fitted and measures the “expected lifetime”.

[0108] It will be apparent to those skilled in the art that the above methods, in particular the statistical methods, described above, are not limited to the identification of markers associated with breast cancer, but may be used to identify set of marker genes associated with any phenotype. The phenotype can be the presence or absence of a disease such as cancer, or the presence or absence of any identifying clinical condition associated with that cancer. In the disease context, the phenotype may be a prognosis such as a survival time, probability of distant metastases of a disease condition, or likelihood of a particular response to a therapeutic or prophylactic regimen. The phenotype need not be cancer, or a disease; the phenotype may be a nominal characteristic associated with a healthy individual.

5.3.3 Sample Collection

[0109] In the present invention, target polynucleotide molecules are extracted from a sample taken from an individual afflicted with breast cancer. The sample may be collected in any clinically acceptable manner, but must be collected such that marker-derived polynucleotides (i.e., RNA) are preserved. mRNA or nucleic acids derived therefrom (i.e., cDNA or amplified DNA) are preferably labeled distinguishably from standard or control polynucleotide molecules, and both are simultaneously or independently hybridized to a microarray comprising some or all of the markers or marker sets or subsets described above. Alternatively, mRNA or nucleic acids derived therefrom may be labeled with the same label as the standard or control polynucleotide molecules, wherein the intensity of hybridization of each at a particular probe is compared. A sample may comprise any clinically relevant tissue sample, such as a tumor biopsy or fine needle aspirate, or a sample of bodily fluid, such as blood, plasma, serum, lymph, ascitic fluid, cystic fluid, urine or nipple exudate. The sample may be taken from a human, or, in a veterinary context, from non-human animals such as ruminants, horses, swine or sheep, or from domestic companion animals such as felines and canines.

[0110] Methods for preparing total and poly(A)+ RNA are well known and are described generally in Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989)) and Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, vol.2, Current Protocols Publishing, New York (1994)).

[0111] RNA may be isolated from eukaryotic cells by procedures that involve lysis of the cells and denaturation of the proteins contained therein. Cells of interest include wild-type cells (i.e., non-cancerous), drug-exposed wild-type cells, tumor- or tumor-derived cells, modified cells, normal or tumor cell line cells, and drug-exposed modified cells.

[0112] Additional steps may be employed to remove DNA. Cell lysis may be accomplished with a nonionic detergent, followed by microcentrifugation to remove the nuclei and hence the bulk of the cellular DNA. In one embodiment, RNA is extracted from cells of the various types of interest using guanidinium thiocyanate lysis followed by CsCl centrifugation to separate the RNA from DNA (Chirgwin et al., Biochemistry 18:5294-5299 (1979)). Poly(A)+ RNA is selected by selection with oligo-dT cellulose (see Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989). Alternatively, separation of RNA from DNA can be accomplished by organic extraction, for example, with hot phenol or phenol/chloroform/isoamyl alcohol.

[0113] If desired, RNAse inhibitors may be added to the lysis buffer. Likewise, for certain cell types, it may be desirable to add a protein denaturation/digestion step to the protocol.

[0114] For many applications, it is desirable to preferentially enrich mRNA with respect to other cellular RNAs, such as transfer RNA (tRNA) and ribosomal RNA (rRNA). Most mRNAs contain a poly(A) tail at their 3′ end. This allows them to be enriched by affinity chromatography, for example, using oligo(dT) or poly(U) coupled to a solid support, such as cellulose or Sephadex™ (see Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, vol. 2, Current Protocols Publishing, New York (1994). Once bound, poly(A)+ mRNA is eluted from the affinity column using 2 mM EDTA/0.1% SDS.

[0115] The sample of RNA can comprise a plurality of different mRNA molecules, each different mRNA molecule having a different nucleotide sequence. In a specific embodiment, the mRNA molecules in the RNA sample comprise at least 100 different nucleotide sequences. More preferably, the mRNA molecules of the RNA sample comprise mRNA molecules corresponding to each of the marker genes. In another specific embodiment, the RNA sample is a mammalian RNA sample.

[0116] In a specific embodiment, total RNA or mRNA from cells are used in the methods of the invention. The source of the RNA can be cells of a plant or animal, human, mammal, primate, non-human animal, dog, cat, mouse, rat, bird, yeast, eukaryote, prokaryote, etc. In specific embodiments, the method of the invention is used with a sample containing total mRNA or total RNA from 1×10⁶ cells or less. In another embodiment, proteins can be isolated from the foregoing sources, by methods known in the art, for use in expression analysis at the protein level.

[0117] Probes to the homologs of the marker sequences disclosed herein can be employed preferably wherein non-human nucleic acid is being assayed.

5.4 Methods of Using Breast Cancer Marker Sets 5.4.1 Diagnostic Methods

[0118] The present invention provides for methods of using the marker sets to analyze a sample from an individual so as to determine the individual's tumor type or subtype at a molecular level, whether a tumor is of the ER(+) or ER(−) type, and whether the tumor is BRCA1-associated or sporadic. The individual need not actually be afflicted with breast cancer. Essentially, the expression of specific marker genes in the individual, or a sample taken therefrom, is compared to a standard or control. For example, assume two breast cancer-related conditions, X and Y. One can compare the level of expression of breast cancer prognostic markers for condition X in an individual to the level of the marker-derived polynucleotides in a control, wherein the level represents the level of expression exhibited by samples having condition X. In this instance, if the expression of the markers in the individual's sample is substantially (i.e., statistically) different from that of the control, then the individual does not have condition X. Where, as here, the choice is bimodal (i.e., a sample is either X or Y), the individual can additionally be said to have condition Y. Of course, the comparison to a control representing condition Y can also be performed. Preferably both are performed simultaneously, such that each control acts as both a positive and a negative control. The distinguishing result may thus either be a demonstrable difference from the expression levels (i.e., the amount of marker-derived RNA, or polynucleotides derived therefrom) represented by the control, or no significant difference.

[0119] Thus, in one embodiment, the method of determining a particular tumor-related status of an individual comprises the steps of (1) hybridizing labeled target polynucleotides from an individual to a microarray containing one of the above marker sets; (2) hybridizing standard or control polynucleotides molecules to the microarray, wherein the standard or control molecules are differentially labeled from the target molecules; and (3) determining the difference in transcript levels, or lack thereof, between the target and standard or control, wherein the difference, or lack thereof, determines the individual's tumor-related status. In a more specific embodiment, the standard or control molecules comprise marker-derived polynucleotides from a pool of samples from normal individuals, or a pool of tumor samples from individuals having sporadic-type tumors. In a preferred embodiment, the standard or control is an artificially-generated pool of marker-derived polynucleotides, which pool is designed to mimic the level of marker expression exhibited by clinical samples of normal or breast cancer tumor tissue having a particular clinical indication (i.e., cancerous or non-cancerous; ER(+) or ER(−) tumor; BRCA1- or sporadic type tumor). In another specific embodiment, the control molecules comprise a pool derived from normal or breast cancer cell lines.

[0120] The present invention provides sets of markers useful for distinguishing ER(+) from ER(−) tumor types. Thus, in one embodiment of the above method, the level of polynucleotides (i.e., mRNA or polynucleotides derived therefrom) in a sample from an individual, expressed from the markers provided in Table 1 are compared to the level of expression of the same markers from a control, wherein the control comprises marker-related polynucleotides derived from ER(+) samples, ER(−) samples, or both. Preferably, the comparison is to both ER(+) and ER(−), and preferably the comparison is to polynucleotide pools from a number of ER(+) and ER(−) samples, respectively. Where the individual's marker expression most closely resembles or correlates with the ER(+) control, and does not resemble or correlate with the ER(−) control, the individual is classified as ER(+). Where the pool is not pure ER(+) or ER(−), for example, a sporadic pool is used. A set of experiments should be performed in which nucleic acids from individuals with known ER status are hybridized against the pool, in order to define the expression templates for the ER(+) and ER(−) group. Nucleic acids from each individual with unknown ER status are hybridized against the same pool and the expression profile is compared to the templates (s) to determine the individual's ER status.

[0121] The present invention provides sets of markers useful for distinguishing BRCA1-related tumors from sporadic tumors. Thus, the method can be performed substantially as for the ER(+/−) determination, with the exception that the markers are those listed in Tables 3 and 4, and the control markers are a pool of marker-derived polynucleotides BRCA1 tumor samples, and a pool of marker-derived polynucleotides from sporadic tumors. A patient is determined to have a BRCA1 germline mutation where the expression of the individual's marker-derived polynucleotides most closely resemble, or are most closely correlated with, that of the BRCA1 control. Where the control is not pure BRCA1 or sporadic, two templates can be defined in a manner similar to that for ER status, as described above.

[0122] For the above two embodiments of the method, the full set of markers may be used (i.e., the complete set of markers for Tables 1 or 3). In other embodiments, subsets of the markers may be used. In a preferred embodiment, the preferred markers listed in Tables 2 or 4 are used.

[0123] The similarity between the marker expression profile of an individual and that of a control can be assessed a number of ways. In the simplest case, the profiles can be compared visually in a printout of expression difference data. Alternatively, the similarity can be calculated mathematically.

[0124] In one embodiment, the similarity between two patients x and y, or patient x and a template y, expressed as a similarity value, can be calculated using the following equation: $\begin{matrix} \begin{matrix} {S = {1 - \left\lbrack {\sum\limits_{i = 1}^{N_{V}}{\frac{\left( {x_{i} - \overset{\_}{x}} \right)}{\sigma_{x_{i}}}{\frac{\left( {y_{i} - \overset{\_}{y}} \right)}{\sigma_{y_{i}}}/}}} \right.}} \\ \left. \sqrt{\sum\limits_{i = 1}^{N_{V}}{\left( \frac{x_{i} - \overset{\_}{x}}{\sigma_{x_{i}}} \right)^{2}{\sum\limits_{i = 1}^{N_{V}}\left( \frac{y_{i} - \overset{\_}{y}}{\sigma_{y_{i}}} \right)^{2}}}} \right\rbrack \end{matrix} & {{Equation}\quad (5)} \end{matrix}$

[0125] In this equation, x and y are two patients with components of log ratio x_(i) and y_(i), i—1 . . . ,N=4,986. Associated with every value x_(i) is error σ_(x) _(o) . The smaller the value σ_(x) _(i) , the more reliable the measurement $\overset{\_}{x} = {\sum\limits_{i = 1}^{N_{V}}{\frac{x_{i}}{\sigma_{x_{i}}^{2}}/{\sum\limits_{i = 1}^{N_{V}}\frac{1}{\sigma_{x_{i}}^{2}}}}}$

[0126] is the error-weighted arithmetic mean.

[0127] In a preferred embodiment, templates are developed for sample comparison.

[0128] The template is defined as the error-weighted log ratio average of the expression difference for the group of marker genes able to differentiate the particular breast cancer-related condition. For example, templates are defined for ER(+) samples and for ER(−) samples. Next, a classifier parameter is calculated. This parameter may be calculated using either expression level differences between the sample and template, or by calculation of a correlation coefficient. Such a coefficient, P_(i), can be calculated using the following equation:

P _(i)=({right arrow over (z)} _(i) •{right arrow over (y)})/(∥{right arrow over (z)} _(i) ∥·∥{right arrow over (y)}∥)  Equation (1)

[0129] where z_(i) is the expression template i, and y is the expression profile of a patient.

[0130] Thus, in a more specific embodiment, the above method of determining a particular tumor-related status of an individual comprises the steps of (1) hybridizing labeled target polynucleotides from an individual to a microarray containing one of the above marker sets; (2) hybridizing standard or control polynucleotides molecules to the microarray, wherein the standard or control molecules are differentially labeled from the target molecules; and (3) determining the ratio (or difference) of transcript levels between two channels (individual and control), or simply the transcript levels of the individual; and (4) comparing the results from (3) to the predefined templates, wherein said determining is accomplished by means of the statistic of Equation 1 or Equation 5, and wherein the difference, or lack thereof, determines the individual's tumor-related status.

5.4.2 Prognostic Methods

[0131] The present invention provides sets of markers useful for classifying patients with into different prognostic categories. For example, the invention further provides a method for using these markers to determine whether an individual afflicted with breast cancer will have a good or poor clinical prognosis. The present invention further provides a method of further classifying “good prognosis” patients into two groups: those having a “very good prognosis” and those having an “intermediate prognosis.” For each of the above classifications, the invention further provides recommended therapeutic regimens.

[0132] The method can use the complete set of markers listed in Table 5. However, subsets of the markers listed in Table 5 may also be used. In a preferred embodiment, the subset of 70 markers listed in Table 6 is used. At least 5, 10, 15, 20, 25, 30, 40, 50, 60, or all 70 of the markers in Table 6 may be used.

[0133] Classification of a sample as “good prognosis” or “poor prognosis” is accomplished substantially as for the diagnostic markers described above, wherein a template is generated to which the marker expression levels in the sample are compared. Thus, in one embodiment of the above method, the level of polynucleotides (i.e., mRNA or polynucleotides derived therefrom) in a sample from an individual breast cancer patient, expressed from the markers provided in Table 5, is compared to the level of expression of the same markers from a control, wherein the control comprises marker-related polynucleotides derived from breast cancer tumor samples taken from breast cancer patients clinically determined to have a good prognosis (“good prognosis” control), breast cancer patients clinically determined to have a poor prognosis (“poor prognosis” control), or both. The comparison may be to both good prognosis and poor prognosis controls, and preferably the comparison is to polynucleotide pools from a number of good prognosis and poor prognosis samples, respectively. Where the individual's marker expression most closely resembles or correlates with the good prognosis control, and does not resemble or correlate with the poor prognosis control, the individual is classified as having a good prognosis. Where the pool is not pure ‘good prognosis’ or ‘poor prognosis’, a set of experiments should be performed in which nucleic acids from samples from individuals with known outcomes are hybridized against the pool to define the expression templates for the good prognosis and poor prognosis groups. Nucleic acids from each individual with unknown outcome are hybridized against the same pool and the resulting expression profile is compared to the templates to predict its outcome.

[0134] The control or standard may be presented in a number of different formats. For example, the control, or template, to which the expression of marker genes in a breast cancer tumor sample is compared may be the average absolute level of expression of each of the genes in a pool of marker-derived nucleic acids pooled from breast cancer tumor samples obtained from a plurality of breast cancer patients. In this case, the difference between the absolute level of expression of these genes in the control and in a sample from a breast cancer patient provides the degree of similarity or dissimilarity of the level of expression in the patient sample and the control. The absolute level of expression may be measured by the intensity of the hybridization of the nucleic acids to an array. In other embodiments, the values for the expression levels of the markers in both the patient sample and control are transformed (see Section 5.4.3). For example, the expression level value for the patient, and the average expression level value for the pool, for each of the marker genes selected, may be transformed by taking the logarithm of the value. Moreover, the expression level values may be normalized by, for example, dividing by the median hybridization intensity of all of the samples that make up the pool. The control may be derived from hybridization data obtained simultaneously with the patient sample expression data, or may constitute a set of numerical values stores on a computer, or on computer-readable medium.

[0135] In one embodiment, the invention provides for method of determining whether an individual afflicted with breast cancer will likely experience a relapse within five years of initial diagnosis (i.e., whether an individual has a poor prognosis) comprising (1) comparing the level of expression of the markers listed in Table 5 in a sample taken from the individual to the level of the same markers in a standard or control, where the standard or control levels represent those found in an individual with a poor prognosis; and (2) determining whether the level of the marker-related polynucleotides in the sample from the individual is significantly different than that of the control, wherein if no substantial difference is found, the patient has a poor prognosis, and if a substantial difference is found, the patient has a good prognosis. Persons of skill in the art will readily see that the markers associated with good prognosis can also be used as controls. In a more specific embodiment, both controls are run.

[0136] Poor prognosis of breast cancer may indicate that a tumor is relatively aggressive, while good prognosis may indicate that a tumor is relatively nonaggressive. Therefore, the invention provides for a method of determining a course of treatment of a breast cancer patient, comprising determining whether the level of expression of the 231 markers of Table 5, or a subset thereof, correlates with the level of these markers in a sample representing a good prognosis expression pattern or a poor prognosis pattern; and determining a course of treatment, wherein if the expression correlates with the poor prognosis pattern, the tumor is treated as an aggressive tumor.

[0137] Patients having an expression profile correlating with the good prognosis profile may be further divided into “very good prognosis” and “intermediate prognosis” groups. In the original 78 samples used to determine the 70 optimal prognostic marker genes, patients whose expression profile correlated with (i.e., had a correlation coefficient less than 0.40) the average “good prognosis” expression profile were classified as having a “good prognosis.” It was subsequently found that tumors with an expression profile having a coefficient of correlation to the average “good prognosis” expression profile greater than 0.636 developed no distant metastases. These patients may receive a different therapeutic regimen than patients whose tumors have a “good prognosis” expression profile that correlates less strongly to the average “good prognosis” expression profile. Accordingly, patients were classified as having a “very good prognosis” expression profile if the correlation coefficient exceeded 0.636, and an “intermediate prognosis” if their expression profile correlation coefficient was 0.39 or less but less than or equal to 0.636. The data for the 70 genes listed in Table 6 for these 78 patients is listed in Table 7.

[0138] This methodology may be generalized to situations in which data from other groups of patients is used, where a group of patients is to provide clinical and expression data to be used for classification of subsequent breast cancer patients. A group of patients is selected for which clinical and followup data are available for at least five years after initial diagnosis. Preferably the patients in the group are selected as a consecutive series to reduce or eliminate selection bias. Breast cancer tumor samples are taken from each patient, and marker-related polynucleotides are generated. The expression levels of each of the marker genes listed in Table 5 or a subset thereof, preferably at least five of the marker genes listed in Table 6, is determined for each tumor sample (i.e., for each patient) to generate a patient expression profile. Marker-derived polynucleotides from patients within the group clinically determined to have a good prognosis (i.e., no distant metastases within five years of initial diagnosis) are pooled and mean expression levels for each of the prognosis-related marker genes are determined to obtain a control expression profile. Patients are then rank ordered in descending order of similarity of patient expression profiles to the control expression profile to produce a rank-ordered list of patients, where the similarity is a value expressed by a single similarity metric such as a correlation coefficient. A first threshold similarity value is then selected, which divides the group of patients into those predicted to have a good prognosis and those predicted to have a poor prognosis. This first threshold similarity value may be the similarity value that most accurately predicts clinical outcomes (i.e., results in an expression profile classification that results in the fewest misclassifications when compared to actual clinical outcomes), or a similarity value that results in a particular number or percentage of false negatives in the group, where a false negative is an expression-based good prognosis prediction for a breast cancer patient that actually develops a distant metastasis within the five year period after initial diagnosis. A second threshold similarity value is then selected which divides the “good prognosis” group into two groups. This threshold similarity value is determined empirically as the similarity value for the patient highest on the rank-ordered list of patients who actually develops a distant metastasis within the five-year period. This second threshold similarity value divides the “good prognosis” group into a group of patients having a “very good prognosis,” i.e., those having similarity values equal to or higher than the second threshold similarity value, and an “intermediate prognosis” group, i.e., those having a similarity value equal to or greater than the first threshold similarity value, but less than the second threshold similarity value. Patients whose similarity values are less than the first threshold similarity value are classified as having a “poor prognosis.” Subsequent patients may be similarly classified by calculating a similarity value for the patient, where the control is the “good prognosis” template or expression profile, and comparison of this similarity metric to the similarity metrics obtained above.

[0139] Thus, in one embodiment, the invention provides a method for classifying a breast cancer patient according to prognosis, comprising comparing the levels of expression of at least five of the genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient to control levels of expression of said at least five genes; and classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said levels of expression in said cell sample and said control levels. In a more specific embodiment, the second step of this method comprises determining whether said similarity exceeds one or more predetermined threshold values of similarity. In another more specific embodiment of this method, said control levels are the mean levels of expression of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have no distant metastases within five years of initial diagnosis. In another more specific embodiment of this method, said control levels comprise the expression levels of said genes in breast cancer patients who have had no distant metastases within five years of initial diagnosis. In yet another more specific embodiment of this method, said control levels comprise, for each of said at least five of the genes for which markers are listed in Table 5, mean log intensity values stored on a computer. In yet another more specific embodiment of this method, said control levels comprise, for each of said at least five of the genes for which markers are listed in Table 6, mean log intensity values stored on a computer. In another more specific embodiment of this method, said control levels comprise, for each of said at least five genes listed in Table 6, the mean log intensity values that are listed in Table 7. The set of mean log intensity values listed in this table may be used as a “good prognosis” template for any of the prognostic methods described herein. The above method may also compare the level of expression of at least ten, 20, 30, 40, 50, 75, 100 or more genes for which markers listed in Table 5, or may use the 70 preferred genes for which markers are listed in Table 6.

[0140] The present invention also provides for the classification of a breast cancer patient into one of three prognostic categories comprising (a) determining the similarity between the level of expression of at least five of the genes for which markers are listed in Table 5 to control levels of expression to obtain a patient similarity value; (b) providing a first threshold similarity value that differentiates persons having a good prognosis from those having a poor prognosis, and providing determining a second threshold similarity value, where said second threshold similarity value indicates a higher degree of similarity of the expression of said genes to said control than said first similarity value; and (c) classifying the breast cancer patient into a first prognostic category if the patient similarity value exceeds the first and second threshold similarity values, a second prognostic category if the patient similarity value equals or exceeds the first but not the second threshold similarity value, and a third prognostic category if the patient similarity value is less than the first threshold similarity value. In a more specific embodiment, the levels of expression of each of said at least five genes is determined first. As above, the control comprises marker-related polynucleotides derived from breast cancer tumor samples taken from breast cancer patients clinically determined to have a good prognosis (“good prognosis” control), breast cancer patients clinically determined to have a poor prognosis “poor prognosis” control), or both. In a preferred embodiment, the control is a “good prognosis” control or template, i.e., a control or template comprising the mean levels of expression of said genes in breast cancer patients who have had no distant metastases within five years of initial diagnosis. In another more specific embodiment, said control levels comprise a set of values, for example mean log intensity values, preferably normalized, stored on a computer. In a more specific embodiment, said control or template is the set of mean log intensity values shown in Table 7. In another specific embodiment, said determining in step (a) may be accomplished by a method comprising determining the difference between the absolute expression level of each of said genes and the average expression level of the same genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis. In another specific embodiment, said determining in step (a) may be accomplished by a method comprising determining the degree of similarity between the level of expression of each of said genes in a breast cancer tumor sample taken from a breast cancer patient and the level of expression of the same genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis.

[0141] In a specific embodiment of the above method, said first threshold similarity value and said second threshold similarity values are selected by a method comprising (a) rank ordering in descending order said tumor samples that compose said pool of tumor samples by the degree of similarity between the level of expression of said genes in each of said tumor samples to the mean level of expression of the same genes of the remaining tumor samples that compose said pool to obtain a rank-ordered list, said degree of similarity being expressed as a similarity value; (b) determining an acceptable number of false negatives in said classifying, wherein said false negatives are breast cancer patients for whom the expression levels of said at least five of the genes for which markers are listed in Table 5 in said cell sample predicts that said patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis; (c) determining a similarity value above which in said rank ordered list fewer than said acceptable number of tumor samples are false negatives; and (d) selecting said similarity value determined in step (c) as said first threshold similarity value; and (e) selecting a second similarity value, greater than said first similarity value, as said second threshold similarity value. In an even more specific embodiment of this method, said second threshold similarity value is selected in step (e) by a method comprising determining which of said tumor samples, taken from patients having a distant metastasis within five years of initial diagnosis, in said rank ordered list has the greatest similarity value, and selecting said greatest similarity value as said second threshold similarity value. In even more specific embodiments, said first and second threshold similarity values are correlation coefficients, and said first threshold similarity value is 0.4 and said second threshold similarity value is greater than 0.4. In another even more specific embodiment, using the template data provided in Table 7, said first and second threshold similarity values are correlation coefficients, and said second threshold similarity value is 0.636. In another specific embodiment, said first similarity value is a similarity value above which at most 10% false negatives are predicted in a training set of tumors, and said second correlation coefficient is a coefficient above which at most 5% false negatives are predicted in said training set of tumors. In another specific embodiment, said first correlation coefficient is a coefficient above which 10% false negatives are predicted in a training set of tumors, and said second correlation coefficient is a coefficient above which no false negatives are predicted in said training set of tumors. In the above and other embodiments, “false negatives” are patients classified by the expression of the marker genes as having a good prognosis, or who are predicted by such expression to have a good prognosis, but who actually do develop distant metastases within five years.

[0142] In a specific embodiment of the above methods, the first, second and third prognostic categories are “very good prognosis,” “intermediate prognosis,” and “poor prognosis,” respectively. Patients classified into the first prognostic category (“very good prognosis”) are likely not to have a distant metastasis within five years of initial diagnosis. Patients classified as having an “intermediate prognosis” are also unlikely to have a distant metastasis within five years of initial diagnosis, but may be recommended to undergo a different therapeutic regimen than patients having a “very good prognosis” marker gene expression profile (see below). Patients classified into the third prognostic category (“poor prognosis”) are likely to have a distant metastasis within five years of initial diagnosis.

[0143] In a more specific embodiment, the similarity value is the degree of difference between the absolute (i.e., untransformed) level of expression of each of the genes in a tumor sample taken from a breast cancer patient and the mean absolute level of expression of the same genes in a control. In another more specific embodiment, the similarity value is calculated using expression level data that is transformed (see Section 5.4.3). In another more specific embodiment, the similarity value is expressed as a similarity metric, such as a correlation coefficient, representing the similarity between the level of expression of the marker genes in the tumor sample and the mean level of expression of the same genes in a plurality of breast cancer tumor samples taken from breast cancer patients.

[0144] In another specific embodiment, said first and second similarity values are derived from control expression data obtained in the same hybridization experiment as that in which the patient expression level data is obtained. In another specific embodiment, said first and second similarity values are derived from an existing set of expression data. In a more specific embodiment, said first and second correlation coefficients are derived from a mathematical sample pool (see Section 5.4.3; Example 9). For example, comparison of the expression of marker genes in new tumor samples may be compared to the pre-existing template determined for these genes for the 78 patients in the initial study; the template, or average expression levels of each of the seventy genes can be used as a reference or control for any tumor sample. Preferably, the comparison is made to a template comprising the average expression level of at least five of the 70 genes listed in Table 6 for the 44 out of 78 patients clinically determined to have a good prognosis. The coefficient of correlation of the level of expression of these genes in the tumor sample to the 44 “good prognosis” patient template is then determined to produce a tumor correlation coefficient. For this control patient set, two similarity values have been derived: a first correlation coefficient of 0.4 and a second correlation coefficient of 0.636, derived using the 70 marker gene set listed in Table 6. New breast cancer patients whose coefficients of correlation of the expression of these marker genes with the 44-patient “good prognosis” template equal or exceed 0.636 are classified as having a “very good prognosis”; those having a coefficient of correlation of between 0.4 and 0.635 are classified as having an “intermediate prognosis”; and those having a correlation coefficient of 0.39 or less are classified as having a “poor prognosis.”

[0145] Because the above methods may utilize arrays to which fluorescently-labeled marker-derived target nucleic acids are hybridized, the invention also provides a method of classifying a breast cancer patient according to prognosis comprising the steps of (a) contacting first nucleic acids derived from a tumor sample taken from said breast cancer patient, and second nucleic acids derived from two or more tumor samples from breast cancer patients who have had no distant metastases within five years of initial diagnosis, with an array under conditions such that hybridization can occur, detecting at each of a plurality of discrete loci on said array a first fluorescent emission signal from said first nucleic acids and a second fluorescent emission signal from said second nucleic acids that are bound to said array under said conditions, wherein said array comprises at least five of the genes for which markers are listed in Table 5 and wherein at least 50% of the probes on said array are listed in Table 5; (b) calculating the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes; and (c) classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes.

[0146] Once patients have been classified as having a “very good prognosis,” “intermediate prognosis” or “poor prognosis,” this information can be combined with the patient's clinical data to determine an appropriate treatment regimen. In one embodiment, the patient's lymph node metastasis status (i.e., whether the patient is pN+ or pN0) is determined. Patients who are pN0 and have a “very good prognosis” or “intermediate” expression profile may be treated without adjuvant chemotherapy. All other patients should be treated with adjuvant chemotherapy. In a more specific embodiment, the patient's estrogen receptor status is also identified (i.e., whether the patient is ER(+) or ER(−)). Here, patients classified as having an “intermediate prognosis” or “poor prognosis” who are ER(+) are assigned a therapeutic regimen that additionally comprises adjuvant hormonal therapy.

[0147] Thus, the invention provides for a method of assigning a therapeutic regimen to a breast cancer patient, comprising (a) classifying said patient as having a “poor prognosis,” “intermediate prognosis,” or “very good prognosis” on the basis of the levels of expression of at least five of the genes for which markers are listed in Table 5; and (b) assigning said patient a therapeutic regimen, said therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and expression profile. In another embodiment, the invention provides a method for assigning a therapeutic regimen for a breast cancer patient, comprising determining the lymph node status for said patient; determining the level of expression of at least five of the genes listed in Table 5 in a tumor sample from said patient, thereby generating an expression profile; classifying said patient as having a “poor prognosis”, “intermediate prognosis” or “very good prognosis” on the basis of said expression profile; and assigning the patient a therapeutic regimen, said therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or a therapeutic regiment comprising chemotherapy if said patient has any other combination of lymph node status and expression profile. In a more specific embodiment of the above methods, the ER status of the patient is additionally determined, and if the breast cancer patient is ER(+) and has an intermediate or poor prognosis, the therapeutic regimen additionally comprises hormonal therapy. Because in the training set of 78 breast cancer patients it was determined that the great majority of intermediate prognosis patients were also ER(+) (see Example 10), another more specific embodiment is to determine the lymph node status and expression profiles, and to assign intermediate prognosis patients adjuvant hormonal therapy (whether or not ER status has been determined). In another specific embodiment, the breast cancer patient is 52 years of age or younger. In another specific embodiment, the breast cancer patient is premenopausal. In another specific embodiment, the breast cancer patient has stage I or stage II breast cancer.

[0148] The use of marker sets is not restricted to the prognosis of breast cancer-related conditions, and may be applied in a variety of phenotypes or conditions, clinical or experimental, in which gene expression plays a role. Where a set of markers has been identified that corresponds to two or more phenotypes, the marker set can be used to distinguish these phenotypes. For example, the phenotypes may be the diagnosis and/or prognosis of clinical states or phenotypes associated with other cancers, other disease conditions, or other physiological conditions, wherein the expression level data is derived from a set of genes correlated with the particular physiological or disease condition. Further, the expression of markers specific to other types of cancer may be used to differentiate patients or patient populations for those cancers for which different therapeutic regimens are indicated.

5.4.3 Improving Sensitivity to Expression Level Differences

[0149] In using the markers disclosed herein, and, indeed, using any sets of markers to differentiate an individual having one phenotype from another individual having a second phenotype, one can compare the absolute expression of each of the markers in a sample to a control; for example, the control can be the average level of expression of each of the markers, respectively, in a pool of individuals. To increase the sensitivity of the comparison, however, the expression level values are preferably transformed in a number of ways.

[0150] For example, the expression level of each of the markers can be normalized by the average expression level of all markers the expression level of which is determined, or by the average expression level of a set of control genes. Thus, in one embodiment, the markers are represented by probes on a microarray, and the expression level of each of the markers is normalized by the mean or median expression level across all of the genes represented on the microarray, including any non-marker genes. In a specific embodiment, the normalization is carried out by dividing the median or mean level of expression of all of the genes on the microarray. In another embodiment, the expression levels of the markers is normalized by the mean or median level of expression of a set of control markers. In a specific embodiment, the control markers comprise a set of housekeeping genes. In another specific embodiment, the normalization is accomplished by dividing by the median or mean expression level of the control genes.

[0151] The sensitivity of a marker-based assay will also be increased if the expression levels of individual markers are compared to the expression of the same markers in a pool of samples. Preferably, the comparison is to the mean or median expression level of each the marker genes in the pool of samples. Such a comparison may be accomplished, for example, by dividing by the mean or median expression level of the pool for each of the markers from the expression level each of the markers in the sample. This has the effect of accentuating the relative differences in expression between markers in the sample and markers in the pool as a whole, making comparisons more sensitive and more likely to produce meaningful results that the use of absolute expression levels alone. The expression level data may be transformed in any convenient way; preferably, the expression level data for all is log transformed before means or medians are taken.

[0152] In performing comparisons to a pool, two approaches may be used. First, the expression levels of the markers in the sample may be compared to the expression level of those markers in the pool, where nucleic acid derived from the sample and nucleic acid derived from the pool are hybridized during the course of a single experiment. Such an approach requires that new pool nucleic acid be generated for each comparison or limited numbers of comparisons, and is therefore limited by the amount of nucleic acid available.

[0153] Alternatively, and preferably, the expression levels in a pool, whether normalized and/or transformed or not, are stored on a computer, or on computer-readable media, to be used in comparisons to the individual expression level data from the sample (i.e., single-channel data).

[0154] Thus, the current invention provides the following method of classifying a first cell or organism as having one of at least two different phenotypes, where the different phenotypes comprise a first phenotype and a second phenotype. The level of expression of each of a plurality of genes in a first sample from the first cell or organism is compared to the level of expression of each of said genes, respectively, in a pooled sample from a plurality of cells or organisms, the plurality of cells or organisms comprising different cells or organisms exhibiting said at least two different phenotypes, respectively, to produce a first compared value. The first compared value is then compared to a second compared value, wherein said second compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having said first phenotype to the level of expression of each of said genes, respectively, in the pooled sample. The first compared value is then compared to a third compared value, wherein said third compared value is the product of a method comprising comparing the level of expression of each of the genes in a sample from a cell or organism characterized as having the second phenotype to the level of expression of each of the genes, respectively, in the pooled sample. Optionally, the first compared value can be compared to additional compared values, respectively, where each additional compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having a phenotype different from said first and second phenotypes but included among the at least two different phenotypes, to the level of expression of each of said genes, respectively, in said pooled sample. Finally, a determination is made as to which of said second, third, and, if present, one or more additional compared values, said first compared value is most similar, wherein the first cell or organism is determined to have the phenotype of the cell or organism used to produce said compared value most similar to said first compared value.

[0155] In a specific embodiment of this method, the compared values are each ratios of the levels of expression of each of said genes. In another specific embodiment, each of the levels of expression of each of the genes in the pooled sample are normalized prior to any of the comparing steps. In a more specific embodiment, the normalization of the levels of expression is carried out by dividing by the median or mean level of the expression of each of the genes or dividing by the mean or median level of expression of one or more housekeeping genes in the pooled sample from said cell or organism. In another specific embodiment, the normalized levels of expression are subjected to a log transform, and the comparing steps comprise subtracting the log transform from the log of the levels of expression of each of the genes in the sample. In another specific embodiment, the two or more different phenotypes are different stages of a disease or disorder. In still another specific embodiment, the two or more different phenotypes are different prognoses of a disease or disorder. In yet another specific embodiment, the levels of expression of each of the genes, respectively, in the pooled sample or said levels of expression of each of said genes in a sample from the cell or organism characterized as having the first phenotype, second phenotype, or said phenotype different from said first and second phenotypes, respectively, are stored on a computer or on a computer-readable medium.

[0156] In another specific embodiment, the two phenotypes are ER(+) or ER(−) status. In another specific embodiment, the two phenotypes are BRCA1 or sporadic tumor-type status. In yet another specific embodiment, the two phenotypes are good prognosis and poor prognosis.

[0157] In another specific embodiment, the comparison is made between the expression of each of the genes in the sample and the expression of the same genes in a pool representing only one of two or more phenotypes. In the context of prognosis-correlated genes, for example, one can compare the expression levels of prognosis-related genes in a sample to the average level of the expression of the same genes in a “good prognosis” pool of samples (as opposed to a pool of samples that include samples from patients having poor prognoses and good prognoses). Thus, in this method, a sample is classified as having a good prognosis if the level of expression of prognosis-correlated genes exceeds a chosen coefficient of correlation to the average “good prognosis” expression profile (i.e., the level of expression of prognosis-correlated genes in a pool of samples from patients having a “good prognosis.” Patients whose expression levels correlate more poorly with the “good prognosis” expression profile (i.e., whose correlation coefficient fails to exceed the chosen coefficient) are classified as having a poor prognosis. The method can be applied to subdivisions of these prognostic classes. For example, in a specific embodiment, the phenotype is good prognosis and said determination comprises (1) determining the coefficient of correlation between the expression of said plurality of genes in the sample and of the same genes in said pooled sample; (2) selecting a first correlation coefficient value between 0.4 and +1 and a second correlation coefficient value between 0.4 and +1, wherein said second value is larger than said first value; and (3) classifying said sample as “very good prognosis” if said coefficient of correlation equals or is greater than said second correlation coefficient value, “intermediate prognosis” if said coefficient of correlation equals or exceeds said first correlation coefficient value, and is less than said second correlation coefficient value, or “poor prognosis” if said coefficient of correlation is less than said first correlation coefficient value.

[0158] Of course, single-channel data may also be used without specific comparison to a mathematical sample pool. For example, a sample may be classified as having a first or a second phenotype, wherein the first and second phenotypes are related, by calculating the similarity between the expression of at least 5 markers in the sample, where the markers are correlated with the first or second phenotype, to the expression of the same markers in a first phenotype template and a second phenotype template, by (a) labeling nucleic acids derived from a sample with a fluorophore to obtain a pool of fluorophore-labeled nucleic acids; (b) contacting said fluorophore-labeled nucleic acid with a microarray under conditions such that hybridization can occur, detecting at each of a plurality of discrete loci on the microarray a flourescent emission signal from said fluorophore-labeled nucleic acid that is bound to said microarray under said conditions; and (c) determining the similarity of marker gene expression in the individual sample to the first and second templates, wherein if said expression is more similar to the first template, the sample is classified as having the first phenotype, and if said expression is more similar to the second template, the sample is classified as having the second phenotype.

5.5 Determination of Marker Gene Expression Levels 5.5.1 Methods

[0159] The expression levels of the marker genes in a sample may be determined by any means known in the art. The expression level may be determined by isolating and determining the level (i.e., amount) of nucleic acid transcribed from each marker gene. Alternatively, or additionally, the level of specific proteins translated from mRNA transcribed from a marker gene may be determined.

[0160] The level of expression of specific marker genes can be accomplished by determining the amount of mRNA, or polynucleotides derived therefrom, present in a sample. Any method for determining RNA levels can be used. For example, RNA is isolated from a sample and separated on an agarose gel. The separated RNA is then transferred to a solid support, such as a filter. Nucleic acid probes representing one or more markers are then hybridized to the filter by northern hybridization, and the amount of marker-derived RNA is determined. Such determination can be visual, or machine-aided, for example, by use of a densitometer. Another method of determining RNA levels is by use of a dot-blot or a slot-blot. In this method, RNA, or nucleic acid derived therefrom, from a sample is labeled. The RNA or nucleic acid derived therefrom is then hybridized to a filter containing oligonucleotides derived from one or more marker genes, wherein the oligonucleotides are placed upon the filter at discrete, easily-identifiable locations. Hybridization, or lack thereof, of the labeled RNA to the filter-bound oligonucleotides is determined visually or by densitometer. Polynucleotides can be labeled using a radiolabel or a fluorescent (i.e., visible) label.

[0161] These examples are not intended to be limiting; other methods of determining RNA abundance are known in the art.

[0162] The level of expression of particular marker genes may also be assessed by determining the level of the specific protein expressed from the marker genes. This can be accomplished, for example, by separation of proteins from a sample on a polyacrylamide gel, followed by identification of specific marker-derived proteins using antibodies in a western blot. Alternatively, proteins can be separated by two-dimensional gel electrophoresis systems. Two-dimensional gel electrophoresis is well-known in the art and typically involves isoelectric focusing along a first dimension followed by SDS-PAGE electrophoresis along a second dimension. See, e.g., Hames et al, 1990, GEL ELECTROPHORESIS OF PROTEINS: A PRACTICAL APPROACH, IRL Press, New York; Shevchenko et al., Proc. Nat'l Acad. Sci. USA 93:1440-1445 (1996); Sagliocco et al., Yeast 12:1519-1533 (1996); Lander, Science 274:536-539 (1996). The resulting electropherograms can be analyzed by numerous techniques, including mass spectrometric techniques, western blotting and immunoblot analysis using polyclonal and monoclonal antibodies.

[0163] Alternatively, marker-derived protein levels can be determined by constructing an antibody microarray in which binding sites comprise immobilized, preferably monoclonal, antibodies specific to a plurality of protein species encoded by the cell genome. Preferably, antibodies are present for a substantial fraction of the marker-derived proteins of interest. Methods for making monoclonal antibodies are well known (see, e.g., Harlow and Lane, 1988, ANTIBODIES: A LABORATORY MANUAL, Cold Spring Harbor, N.Y., which is incorporated in its entirety for all purposes). In one embodiment, monoclonal antibodies are raised against synthetic peptide fragments designed based on genomic sequence of the cell. With such an antibody array, proteins from the cell are contacted to the array, and their binding is assayed with assays known in the art. Generally, the expression, and the level of expression, of proteins of diagnostic or prognostic interest can be detected through immunohistochemical staining of tissue slices or sections.

[0164] Finally, expression of marker genes in a number of tissue specimens may be characterized using a “tissue array” (Kononen et al., Nat. Med 4(7):844-7 (1998)). In a tissue array, multiple tissue samples are assessed on the same microarray. The arrays allow in situ detection of RNA and protein levels; consecutive sections allow the analysis of multiple samples simultaneously.

5.5.2 Microarrays

[0165] In preferred embodiments, polynucleotide microarrays are used to measure expression so that the expression status of each of the markers above is assessed simultaneously. In a specific embodiment, the invention provides for oligonucleotide or cDNA arrays comprising probes hybridizable to the genes corresponding to each of the marker sets described above (i.e., markers to determine the molecular type or subtype of a tumor; markers to distinguish ER status; markers to distinguish BRCA1 from sporadic tumors; markers to distinguish patients with good versus patients with poor prognosis; markers to distinguish both ER(+) from ER(−), and BRCA1 tumors from sporadic tumors; markers to distinguish ER(+) from ER(−), and patients with good prognosis from patients with poor prognosis; markers to distinguish BRCA1 tumors from sporadic tumors, and patients with good prognosis from patients with poor prognosis; and markers able to distinguish ER(+) from ER(−), BRCA1 tumors from sporadic tumors, and patients with good prognosis from patients with poor prognosis; and markers unique to each status).

[0166] The microarrays provided by the present invention may comprise probes hybridizable to the genes corresponding to markers able to distinguish the status of one, two, or all three of the clinical conditions noted above. In particular, the invention provides polynucleotide arrays comprising probes to a subset or subsets of at least 50, 100, 200, 300, 400, 500, 750, 1,000, 1,250, 1,500, 1,750, 2,000 or 2,250 genetic markers, up to the full set of 2,460 markers, which distinguish ER(+) and ER(−) patients or tumors. The invention also provides probes to subsets of at least 20, 30, 40, 50, 75, 100, 150, 200, 250, 300, 350 or 400 markers, up to the full set of 430 markers, which distinguish between tumors containing a BRCA1 mutation and sporadic tumors within an ER(−) group of tumors. The invention also provides probes to subsets of at least 20, 30, 40, 50, 75, 100, 150 or 200 markers, up to the full set of 231 markers, which distinguish between patients with good and poor prognosis within sporadic tumors. In a specific embodiment, the array comprises probes to marker sets or subsets directed to any two of the clinical conditions. In a more specific embodiment, the array comprises probes to marker sets or subsets directed to all three clinical conditions.

[0167] In specific embodiments, the invention provides polynucleotide arrays in which the breast cancer-related markers described herein comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95% or 98% of the probes on said array. In another specific embodiment, the invention provides polynucleotide arrays in which ER status-related markers selected from Table 1 comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95% or 98% of the probes on said array. In another specific embodiment, the invention provides polynucleotide arrays in which BRCA1/sporadic markers selected from Table 3 comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95% or 98% of the probes on said array. In another specific embodiment, the invention provides polynucleotide arrays in which prognostic markers selected from Table 5 comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95% or 98% of the probes on said array.

[0168] In yet another specific embodiment, microarrays that are used in the methods disclosed herein optionally comprise markers additional to at least some of the markers listed in Tables 1-6. For example, in a specific embodiment, the microarray is a screening or scanning array as described in Altschuler et al., International Publication WO 02/18646, published Mar. 7, 2002 and Scherer et al., International Publication WO 02/16650, published Feb. 28, 2002. The scanning and screening arrays comprise regularly-spaced, positionally-addressable probes derived from genomic nucleic acid sequence, both expressed and unexpressed. Such arrays may comprise probes corresponding to a subset of, or all of, the markers listed in Tables 1-6, or a subset thereof as described above, and can be used to monitor marker expression in the same way as a microarray containing only markers listed in Tables 1-6.

[0169] In yet another specific embodiment, the microarray is a commercially-available cDNA microarray that comprises at least five of the markers listed in Tables 1-6. Preferably, a commercially-available cDNA microarray comprises all of the markers listed in Tables 1-6. However, such a microarray may comprise 5, 10, 15, 25, 50, 100, 150, 250, 500, 1000 or more of the markers in any of Tables 1-6, up to the maximum number of markers in a Table, and may comprise all of the markers in any one of Tables 1-6 and a subset of another of Tables 1-6, or subsets of each as described above. In a specific embodiment of the microarrays used in the methods disclosed herein, the markers that are all or a portion of Tables 1-6 make up at least 50%, 60%, 70%, 80%, 90%, 95% or 98% of the probes on the microarray.

[0170] General methods pertaining to the construction of microarrays comprising the marker sets and/or subsets above are described in the following sections.

5.5.2.1 Construction of Microarrays

[0171] Microarrays are prepared by selecting probes which comprise a polynucleotide sequence, and then immobilizing such probes to a solid support or surface. For example, the probes may comprise DNA sequences, RNA sequences, or copolymer sequences of DNA and RNA. The polynucleotide sequences of the probes may also comprise DNA and/or RNA analogues, or combinations thereof. For example, the polynucleotide sequences of the probes may be full or partial fragments of genomic DNA. The polynucleotide sequences of the probes may also be synthesized nucleotide sequences, such as synthetic oligonucleotide sequences. The probe sequences can be synthesized either enzymatically in vivo, enzymatically in vitro (e.g., by PCR), or non-enzymatically in vitro.

[0172] The probe or probes used in the methods of the invention are preferably immobilized to a solid support which may be either porous or non-porous. For example, the probes of the invention may be polynucleotide sequences which are attached to a nitrocellulose or nylon membrane or filter covalently at either the 3′ or the 5′ end of the polynucleotide. Such hybridization probes are well known in the art (see, e.g., Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989). Alternatively, the solid support or surface may be a glass or plastic surface. In a particularly preferred embodiment, hybridization levels are measured to microarrays of probes consisting of a solid phase on the surface of which are immobilized a population of polynucleotides, such as a population of DNA or DNA mimics, or, alternatively, a population of RNA or RNA mimics. The solid phase may be a nonporous or, optionally, a porous material such as a gel.

[0173] In preferred embodiments, a microarray comprises a support or surface with an ordered array of binding (e.g., hybridization) sites or “probes” each representing one of the markers described herein. Preferably the microarrays are addressable arrays, and more preferably positionally addressable arrays. More specifically, each probe of the array is preferably located at a known, predetermined position on the solid support such that the identity (i.e., the sequence) of each probe can be determined from its position in the array (i.e., on the support or surface). In preferred embodiments, each probe is covalently attached to the solid support at a single site.

[0174] Microarrays can be made in a number of ways, of which several are described below. However produced, microarrays share certain characteristics. The arrays are reproducible, allowing multiple copies of a given array to be produced and easily compared with each other. Preferably, microarrays are made from materials that are stable under binding (e.g., nucleic acid hybridization) conditions. The microarrays are preferably small, e.g., between 1 cm² and 25 cm², between 12 cm² and 13 cm², or 3 cm². However, larger arrays are also contemplated and may be preferable, e.g., for use in screening arrays. Preferably, a given binding site or unique set of binding sites in the microarray will specifically bind (e.g., hybridize) to the product of a single gene in a cell (e.g., to a specific mRNA, or to a specific cDNA derived therefrom). However, in general, other related or similar sequences will cross hybridize to a given binding site.

[0175] The microarrays of the present invention include one or more test probes, each of which has a polynucleotide sequence that is complementary to a subsequence of RNA or DNA to be detected. Preferably, the position of each probe on the solid surface is known. Indeed, the microarrays are preferably positionally addressable arrays. Specifically, each probe of the array is preferably located at a known, predetermined position on the solid support such that the identity (i.e., the sequence) of each probe can be determined from its position on the array (i.e., on the support or surface).

[0176] According to the invention, the microarray is an array (i.e., a matrix) in which each position represents one of the markers described herein. For example, each position can contain a DNA or DNA analogue based on genomic DNA to which a particular RNA or cDNA transcribed from that genetic marker can specifically hybridize. The DNA or DNA analogue can be, e.g., a synthetic oligomer or a gene fragment. In one embodiment, probes representing each of the markers is present on the array. In a preferred embodiment, the array comprises the 550 of the 2,460 RE-status markers, 70 of the BRCA1/sporadic markers, and all 231 of the prognosis markers.

5.5.2.2 Preparing Probes for Microarrays

[0177] As noted above, the “probe” to which a particular polynucleotide molecule specifically hybridizes according to the invention contains a complementary genomic polynucleotide sequence. The probes of the microarray preferably consist of nucleotide sequences of no more than 1,000 nucleotides. In some embodiments, the probes of the array consist of nucleotide sequences of 10 to 1,000 nucleotides. In a preferred embodiment, the nucleotide sequences of the probes are in the range of 10-200 nucleotides in length and are genomic sequences of a species of organism, such that a plurality of different probes is present, with sequences complementary and thus capable of hybridizing to the genome of such a species of organism, sequentially tiled across all or a portion of such genome. In other specific embodiments, the probes are in the range of 10-30 nucleotides in length, in the range of 10-40 nucleotides in length, in the range of 20-50 nucleotides in length, in the range of 40-80 nucleotides in length, in the range of 50-150 nucleotides in length, in the range of 80-120 nucleotides in length, and most preferably are 60 nucleotides in length.

[0178] The probes may comprise DNA or DNA “mimics” (e.g., derivatives and analogues) corresponding to a portion of an organism's genome. In another embodiment, the probes of the microarray are complementary RNA or RNA mimics. DNA mimics are polymers composed of subunits capable of specific, Watson-Crick-like hybridization with DNA, or of specific hybridization with RNA. The nucleic acids can be modified at the base moiety, at the sugar moiety, or at the phosphate backbone. Exemplary DNA mimics include, e.g., phosphorothioates.

[0179] DNA can be obtained, e.g., by polymerase chain reaction (PCR) amplification of genomic DNA or cloned sequences. PCR primers are preferably chosen based on a known sequence of the genome that will result in amplification of specific fragments of genomic DNA. Computer programs that are well known in the art are useful in the design of primers with the required specificity and optimal amplification properties, such as Oligo version 5.0 (National Biosciences). Typically each probe on the microarray will be between 10 bases and 50,000 bases, usually between 300 bases and 1,000 bases in length. PCR methods are well known in the art, and are described, for example, in Innis et al., eds., PCR PROTOCOLS: A GUIDE TO METHODS AND APPLICATIONS, Academic Press Inc., San Diego, Calif. (1990). It will be apparent to one skilled in the art that controlled robotic systems are useful for isolating and amplifying nucleic acids.

[0180] An alternative, preferred means for generating the polynucleotide probes of the microarray is by synthesis of synthetic polynucleotides or oligonucleotides, e.g., using N-phosphonate or phosphoramidite chemistries (Froehler et al., Nucleic Acid Res. 14:5399-5407 (1986); McBride et al., Tetrahedron Lett. 24:246-248 (1983)). Synthetic sequences are typically between about 10 and about 500 bases in length, more typically between about 20 and about 100 bases, and most preferably between about 40 and about 70 bases in length. In some embodiments, synthetic nucleic acids include non-natural bases, such as, but by no means limited to, inosine. As noted above, nucleic acid analogues may be used as binding sites for hybridization. An example of a suitable nucleic acid analogue is peptide nucleic acid (see, e.g., Egholm et al., Nature 363:566-568 (1993); U.S. Pat. No. 5,539,083). Probes are preferably selected using an algorithm that takes into account binding energies, base composition, sequence complexity, cross-hybridization binding energies, and secondary structure (see Friend et al., International Patent Publication WO 01/05935, published Jan. 25, 2001; Hughes et al., Nat. Biotech. 19:342-7 (2001)).

[0181] A skilled artisan will also appreciate that positive control probes, e.g., probes known to be complementary and hybridizable to sequences in the target polynucleotide molecules, and negative control probes, e.g., probes known to not be complementary and hybridizable to sequences in the target polynucleotide molecules, should be included on the array. In one embodiment, positive controls are synthesized along the perimeter of the array. In another embodiment, positive controls are synthesized in diagonal stripes across the array. In still another embodiment, the reverse complement for each probe is synthesized next to the position of the probe to serve as a negative control. In yet another embodiment, sequences from other species of organism are used as negative controls or as “spike-in” controls.

5.5.2.3 Attaching Probes to the Solid Surface

[0182] The probes are attached to a solid support or surface, which may be made, e.g., from glass, plastic (e.g., polypropylene, nylon), polyacrylamide, nitrocellulose, gel, or other porous or nonporous material. A preferred method for attaching the nucleic acids to a surface is by printing on glass plates, as is described generally by Schena et al, Science 270:467-470 (1995). This method is especially useful for preparing microarrays of cDNA (See also, DeRisi et al, Nature Genetics 14:457-460 (1996); Shalon et al., Genome Res. 6:639-645 (1996); and Schena et al., Proc. Natl. Acad. Sci. U.S.A. 93:10539-11286 (1995)).

[0183] A second preferred method for making microarrays is by making high-density oligonucleotide arrays. Techniques are known for producing arrays containing thousands of oligonucleotides complementary to defined sequences, at defined locations on a surface using photolithographic techniques for synthesis in situ (see, Fodor et al., 1991, Science 251:767-773; Pease et al., 1994, Proc. Natl. Acad. Sci. U.S.A. 91:5022-5026; Lockhart et al., 1996, Nature Biotechnology 14:1675; U.S. Pat. Nos. 5,578,832; 5,556,752; and 5,510,270) or other methods for rapid synthesis and deposition of defined oligonucleotides (Blanchard et al., Biosensors & Bioelectronics 11:687-690). When these methods are used, oligonucleotides (e.g., 60-mers) of known sequence are synthesized directly on a surface such as a derivatized glass slide. Usually, the array produced is redundant, with several oligonucleotide molecules per RNA.

[0184] Other methods for making microarrays, e.g., by masking (Maskos and Southern, 1992, Nuc. Acids. Res. 20:1679-1684), may also be used. In principle, and as noted supra, any type of array, for example, dot blots on a nylon hybridization membrane (see Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989)) could be used. However, as will be recognized by those skilled in the art, very small arrays will frequently be preferred because hybridization volumes will be smaller.

[0185] In one embodiment, the arrays of the present invention are prepared by synthesizing polynucleotide probes on a support. In such an embodiment, polynucleotide probes are attached to the support covalently at either the 3′ or the 5′ end of the polynucleotide.

[0186] In a particularly preferred embodiment, microarrays of the invention are manufactured by means of an ink jet printing device for oligonucleotide synthesis, e.g., using the methods and systems described by Blanchard in U.S. Pat. No. 6,028,189; Blanchard et al., 1996, Biosensors and Bioelectronics 11:687-690; Blanchard, 1998, in SYNTHETIC DNA ARRAYS IN GENETIC ENGINEERING, Vol. 20, J. K. Setlow, Ed., Plenum Press, New York at pages 111-123. Specifically, the oligonucleotide probes in such microarrays are preferably synthesized in arrays, e.g., on a glass slide, by serially depositing individual nucleotide bases in “microdroplets” of a high surface tension solvent such as propylene carbonate. The microdroplets have small volumes (e.g., 100 pL or less, more preferably 50 pL or less) and are separated from each other on the microarray (e.g., by hydrophobic domains) to form circular surface tension wells which define the locations of the array elements (i.e., the different probes). Microarrays manufactured by this ink-jet method are typically of high density, preferably having a density of at least about 2,500 different probes per 1 cm². The polynucleotide probes are attached to the support covalently at either the 3′ or the 5′ end of the polynucleotide.

5.5.2.4 Target Polynucleotide Molecules

[0187] The polynucleotide molecules which may be analyzed by the present invention (the “target polynucleotide molecules”) may be from any clinically relevant source, but are expressed RNA or a nucleic acid derived therefrom (e.g., cDNA or amplified RNA derived from cDNA that incorporates an RNA polymerase promoter), including naturally occurring nucleic acid molecules, as well as synthetic nucleic acid molecules. In one embodiment, the target polynucleotide molecules comprise RNA, including, but by no means limited to, total cellular RNA, poly(A)⁺ messenger RNA (mRNA) or fraction thereof, cytoplasmic mRNA, or RNA transcribed from cDNA (i.e., cRNA; see, e.g., Linsley & Schelter, U.S. patent application Ser. No. 09/411,074, filed Oct. 4, 1999, or U.S. Pat. Nos. 5,545,522, 5,891,636, or 5,716,785). Methods for preparing total and poly(A)⁺ RNA are well known in the art, and are described generally, e.g., in Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989). In one embodiment, RNA is extracted from cells of the various types of interest in this invention using guanidinium thiocyanate lysis followed by CsCl centrifugation (Chirgwin et al., 1979, Biochemistry 18:5294-5299). In another embodiment, total RNA is extracted using a silica gel-based column, commercially available examples of which include RNeasy (Qiagen, Valencia, Calif.) and StrataPrep (Stratagene, La Jolla, Calif.). In an alternative embodiment, which is preferred for S. cerevisiae, RNA is extracted from cells using phenol and chloroform, as described in Ausubel et al., eds., 1989, CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, Vol III, Green Publishing Associates, Inc., John Wiley & Sons, Inc., New York, at pp. 13.12.1-13.12.5). Poly(A)⁺ RNA can be selected, e.g., by selection with oligo-dT cellulose or, alternatively, by oligo-dT primed reverse transcription of total cellular RNA. In one embodiment, RNA can be fragmented by methods known in the art, e.g., by incubation with ZnCl₂, to generate fragments of RNA. In another embodiment, the polynucleotide molecules analyzed by the invention comprise cDNA, or PCR products of amplified RNA or cDNA.

[0188] In one embodiment, total RNA, mRNA, or nucleic acids derived therefrom, is isolated from a sample taken from a person afflicted with breast cancer. Target polynucleotide molecules that are poorly expressed in particular cells may be enriched using normalization techniques (Bonaldo et al., 1996, Genome Res. 6:791-806).

[0189] As described above, the target polynucleotides are detectably labeled at one or more nucleotides. Any method known in the art may be used to detectably label the target polynucleotides. Preferably, this labeling incorporates the label uniformly along the length of the RNA, and more preferably, the labeling is carried out at a high degree of efficiency. One embodiment for this labeling uses oligo-dT primed reverse transcription to incorporate the label; however, conventional methods of this method are biased toward generating 3′ end fragments. Thus, in a preferred embodiment, random primers (e.g., 9-mers) are used in reverse transcription to uniformly incorporate labeled nucleotides over the full length of the target polynucleotides. Alternatively, random primers may be used in conjunction with PCR methods or T7 promoter-based in vitro transcription methods in order to amplify the target polynucleotides.

[0190] In a preferred embodiment, the detectable label is a luminescent label. For example, fluorescent labels, bioluminescent labels, chemiluminescent labels, and colorimetric labels may be used in the present invention. In a highly preferred embodiment, the label is a fluorescent label, such as a fluorescein, a phosphor, a rhodamine, or a polymethine dye derivative. Examples of commercially available fluorescent labels include, for example, fluorescent phosphoramidites such as FluorePrime (Amersham Pharmacia, Piscataway, N.J.), Fluoredite (Millipore, Bedford, Mass.), FAM (ABI, Foster City, Calif.), and Cy3 or Cy5 (Amersham Pharmacia, Piscataway, N.J.). In another embodiment, the detectable label is a radiolabeled nucleotide.

[0191] In a further preferred embodiment, target polynucleotide molecules from a patient sample are labeled differentially from target polynucleotide molecules of a standard. The standard can comprise target polynucleotide molecules from normal individuals (i.e., those not afflicted with breast cancer). In a highly preferred embodiment, the standard comprises target polynucleotide molecules pooled from samples from normal individuals or tumor samples from individuals having sporadic-type breast tumors. In another embodiment, the target polynucleotide molecules are derived from the same individual, but are taken at different time points, and thus indicate the efficacy of a treatment by a change in expression of the markers, or lack thereof, during and after the course of treatment (i.e., chemotherapy, radiation therapy or cryotherapy), wherein a change in the expression of the markers from a poor prognosis pattern to a good prognosis pattern indicates that the treatment is efficacious. In this embodiment, different timepoints are differentially labeled.

5.5.2.5 Hybridization to Microarrays

[0192] Nucleic acid hybridization and wash conditions are chosen so that the target polynucleotide molecules specifically bind or specifically hybridize to the complementary polynucleotide sequences of the array, preferably to a specific array site, wherein its complementary DNA is located.

[0193] Arrays containing double-stranded probe DNA situated thereon are preferably subjected to denaturing conditions to render the DNA single-stranded prior to contacting with the target polynucleotide molecules. Arrays containing single-stranded probe DNA (e.g., synthetic oligodeoxyribonucleic acids) may need to be denatured prior to contacting with the target polynucleotide molecules, e.g., to remove hairpins or dimers which form due to self complementary sequences.

[0194] Optimal hybridization conditions will depend on the length (e.g., oligomer versus polynucleotide greater than 200 bases) and type (e.g., RNA, or DNA) of probe and target nucleic acids. One of skill in the art will appreciate that as the oligonucleotides become shorter, it may become necessary to adjust their length to achieve a relatively uniform melting temperature for satisfactory hybridization results. General parameters for specific (i.e., stringent) hybridization conditions for nucleic acids are described in Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989), and in Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, vol. 2, Current Protocols Publishing, New York (1994). Typical hybridization conditions for the cDNA microarrays of Schena et al. are hybridization in 5×SSC plus 0.2% SDS at 65° C. for four hours, followed by washes at 25° C. in low stringency wash buffer (1×SSC plus 0.2% SDS), followed by 10 minutes at 25° C. in higher stringency wash buffer (0.1×SSC plus 0.2% SDS) (Schena et al., Proc. Natl. Acad. Sci. U.S.A. 93:10614 (1993)). Useful hybridization conditions are also provided in, e.g., Tijessen, 1993, HYBRIDIZATION WITH NUCLEIC ACID PROBES, Elsevier Science Publishers B. V.; and Kricka, 1992, NONISOTOPIC DNA PROBE TECHNIQUES, Academic Press, San Diego, Calif.

[0195] Particularly preferred hybridization conditions include hybridization at a temperature at or near the mean melting temperature of the probes (e.g., within 5° C., more preferably within 2° C.) in 1 M NaCl, 50 mM MES buffer (pH 6.5), 0.5% sodium sarcosine and 30% formamide.

5.5.2.6 Signal Detection and Data Analysis

[0196] When fluorescently labeled probes are used, the fluorescence emissions at each site of a microarray may be, preferably, detected by scanning confocal laser microscopy. In one embodiment, a separate scan, using the appropriate excitation line, is carried out for each of the two fluorophores used. Alternatively, a laser may be used that allows simultaneous specimen illumination at wavelengths specific to the two fluorophores and emissions from the two fluorophores can be analyzed simultaneously (see Shalon et al., 1996, “A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization,” Genome Research 6:639-645, which is incorporated by reference in its entirety for all purposes). In a preferred embodiment, the arrays are scanned with a laser fluorescent scanner with a computer controlled X-Y stage and a microscope objective. Sequential excitation of the two fluorophores is achieved with a multi-line, mixed gas laser and the emitted light is split by wavelength and detected with two photomultiplier tubes. Fluorescence laser scanning devices are described in Schena et al., Genome Res. 6:639-645 (1996), and in other references cited herein. Alternatively, the fiber-optic bundle described by Ferguson et al., Nature Biotech. 14:1681-1684 (1996), may be used to monitor mRNA abundance levels at a large number of sites simultaneously.

[0197] Signals are recorded and, in a preferred embodiment, analyzed by computer, e.g., using a 12 or 16 bit analog to digital board. In one embodiment the scanned image is despeckled using a graphics program (e.g., Hijaak Graphics Suite) and then analyzed using an image gridding program that creates a spreadsheet of the average hybridization at each wavelength at each site. If necessary, an experimentally determined correction for “cross talk” (or overlap) between the channels for the two fluors may be made. For any particular hybridization site on the transcript array, a ratio of the emission of the two fluorophores can be calculated. The ratio is independent of the absolute expression level of the cognate gene, but is useful for genes whose expression is significantly modulated in association with the different breast cancer-related condition.

5.6 Computer-Facilitated Analysis

[0198] The present invention further provides for kits comprising the marker sets above. In a preferred embodiment, the kit contains a microarray ready for hybridization to target polynucleotide molecules, plus software for the data analyses described above.

[0199] The analytic methods described in the previous sections can be implemented by use of the following computer systems and according to the following programs and methods. A computer system comprises internal components linked to external components. The internal components of a typical computer system include a processor element interconnected with a main memory. For example, the computer system can be an Intel 8086-, 80386-, 80486-, Pentium™, or Pentium™-based processor with preferably 32 MB or more of main memory. The computer system may also be a Macintosh or a Macintosh-based system, but may also be a minicomputer or mainframe.

[0200] The external components may include mass storage. This mass storage can be one or more hard disks (which are typically packaged together with the processor and memory). Such hard disks are preferably of 1 GB or greater storage capacity. Other external components include a user interface device, which can be a monitor, together with an inputting device, which can be a “mouse”, or other graphic input devices, and/or a keyboard. A printing device can also be attached to the computer.

[0201] Typically, a computer system is also linked to network link, which can be part of an Ethernet link to other local computer systems, remote computer systems, or wide area communication networks, such as the Internet. This network link allows the computer system to share data and processing tasks with other computer systems.

[0202] Loaded into memory during operation of this system are several software components, which are both standard in the art and special to the instant invention. These software components collectively cause the computer system to function according to the methods of this invention. These software components are typically stored on the mass storage device. A software component comprises the operating system, which is responsible for managing computer system and its network interconnections. This operating system can be, for example, of the Microsoft Windows® family, such as Windows 3.1, Windows 95, Windows 98, Windows 2000, or Windows NT, or may be of the Macintosh OS family, or may be UNIX or an operating system specific to a minicomputer or mainframe. The software component represents common languages and functions conveniently present on this system to assist programs implementing the methods specific to this invention. Many high or low level computer languages can be used to program the analytic methods of this invention. Instructions can be interpreted during run-time or compiled. Preferred languages include C/C++, FORTRAN and JAVA. Most preferably, the methods of this invention are programmed in mathematical software packages that allow symbolic entry of equations and high-level specification of processing, including some or all of the algorithms to be used, thereby freeing a user of the need to procedurally program individual equations or algorithms. Such packages include Mathlab from Mathworks (Natick, Mass.), Mathematica® from Wolfram Research (Champaign, Ill.), or S-Plus® from Math Soft (Cambridge, Mass.). Specifically, the software component includes the analytic methods of the invention as programmed in a procedural language or symbolic package.

[0203] The software to be included with the kit comprises the data analysis methods of the invention as disclosed herein. In particular, the software may include mathematical routines for marker discovery, including the calculation of similarity values between clinical categories (e.g., ER status) and marker expression. The software may also include mathematical routines for calculating the similarity between sample marker expression and control marker expression, using array-generated fluorescence data, to determine the clinical classification of a sample.

[0204] Additionally, the software may also include mathematical routines for determining the prognostic outcome, and recommended therapeutic regimen, for a particular breast cancer patient. Such software would include instructions for the computer system's processor to receive data structures that include the level of expression of five or more of the marker genes listed in Table 5 in a breast cancer tumor sample obtained from the breast cancer patient; the mean level of expression of the same genes in a control or template; and the breast cancer patient's clinical information, including lymph node and ER status. The software may additionally include mathematical routines for transforming the hybridization data and for calculating the similarity between the expression levels for the marker genes in the patient's breast cancer tumor sample and the control or template. In a specific embodiment, the software includes mathematical routines for calculating a similarity metric, such as a coefficient of correlation, representing the similarity between the expression levels for the marker genes in the patient's breast cancer tumor sample and the control or template, and expressing the similarity as that similarity metric.

[0205] The software would include decisional routines that integrate the patient's clinical and marker gene expression data, and recommend a course of therapy. In one embodiment, for example, the software causes the processor unit to receive expression data for the patient's tumor sample, calculate a metric of similarity of these expression values to the values for the same genes in a template or control, compare this similarity metric to a pre-selected similarity metric threshold or thresholds that differentiate prognostic groups, assign the patient to the prognostic group, and, on the basis of the prognostic group, assign a recommended therapeutic regimen. In a specific example, the software additionally causes the processor unit to receive data structures comprising clinical information about the breast cancer patient. In a more specific example, such clinical information includes the patient's age, stage of breast cancer, estrogen receptor status, and lymph node status.

[0206] Where the control is an expression template comprising expression values for marker genes within a group of breast cancer patients, the control can comprise either hybridization data obtained at the same time (i.e., in the same hybridization experiment) as the patient's individual hybridization data, or can be a set of hybridization or marker expression values stores on a computer, or on computer-readable media. If the latter is used, new patient hybridization data for the selected marker genes, obtained from initial or follow-up tumor samples, or suspected tumor samples, can be compared to the stored values for the same genes without the need for additional control hybridizations. However, the software may additionally comprise routines for updating the control data set, i.e., to add information from additional breast cancer patients or to remove existing members of the control data set, and, consequently, for recalculating the average expression level values that comprise the template. In another specific embodiment, said control comprises a set of single-channel mean hybridization intensity values for each of said at least five of said genes, stored on a computer-readable medium.

[0207] Clinical data relating to a breast cancer patient, and used by the computer program products of the invention, can be contained in a database of clinical data in which information on each patient is maintained in a separate record, which record may contain any information relevant to the patient, the patient's medical history, treatment, prognosis, or participation in a clinical trial or study, including expression profile data generated as part of an initial diagnosis or for tracking the progress of the breast cancer during treatment.

[0208] Thus, one embodiment of the invention provides a computer program product for classifying a breast cancer patient according to prognosis, the computer program product for use in conjunction with a computer having a memory and a processor, the computer program product comprising a computer readable storage medium having a computer program mechanism encoded thereon, wherein said computer program product can be loaded into the one or more memory units of a computer and causes the one or more processor units of the computer to execute the steps of (a) receiving a first data structure comprising the level of expression of at least five of the genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient; (b) determining the similarity of the level of expression of said at least five genes to control levels of expression of said at least five genes to obtain a patient similarity value; (c) comparing said patient similarity value to selected first and second threshold values of similarity of said level of expression of said genes to said control levels of expression to obtain first and second similarity threshold values, respectively, wherein said second similarity threshold indicates greater similarity to said control levels of expression than does said first similarity threshold; and (d) classifying said breast cancer patient as having a first prognosis if said patient similarity value exceeds said first and said second threshold similarity values, a second prognosis if said patient similarity value exceeds said first threshold similarity value but does not exceed said second threshold similarity value, and a third prognosis if said patient similarity value does not exceed said first threshold similarity value or said second threshold similarity value. In a specific embodiment of said computer program product, said first threshold value of similarity and said second threshold value of similarity are values stored in said computer. In another more specific embodiment, said first prognosis is a “very good prognosis,” said second prognosis is an “intermediate prognosis,” and said third prognosis is a “poor prognosis,” and wherein said computer program mechanism may be loaded into the memory and further cause said one or more processor units of said computer to execute the step of assigning said breast cancer patient a therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and expression profile. In another specific embodiment, said computer program mechanism may be loaded into the memory and further cause said one or more processor units of the computer to execute the steps of receiving a data structure comprising clinical data specific to said breast cancer patient. In a more specific embodiment, said clinical data includes the lymph node and estrogen receptor (ER) status of said breast cancer patient. In more specific embodiment, said single-channel hybridization intensity values are log transformed. The computer implementation of the method, however, may use any desired transformation method. In another specific embodiment, the computer program product causes said processing unit to perform said comparing step (c) by calculating the difference between the level of expression of each of said genes in said cell sample taken from said breast cancer patient and the level of expression of the same genes in said control. In another specific embodiment, the computer program product causes said processing unit to perform said comparing step (c) by calculating the mean log level of expression of each of said genes in said control to obtain a control mean log expression level for each gene, calculating the log expression level for each of said genes in a breast cancer sample from said breast cancer patient to obtain a patient log expression level, and calculating the difference between the patient log expression level and the control mean log expression for each of said genes. In another specific embodiment, the computer program product causes said processing unit to perform said comparing step (c) by calculating similarity between the level of expression of each of said genes in said cell sample taken from said breast cancer patient and the level of expression of the same genes in said control, wherein said similarity is expressed as a similarity value. In more specific embodiment, said similarity value is a correlation coefficient. The similarity value may, however, be expressed as any art-known similarity metric.

[0209] In an exemplary implementation, to practice the methods of the present invention, a user first loads experimental data into the computer system. These data can be directly entered by the user from a monitor, keyboard, or from other computer systems linked by a network connection, or on removable storage media such as a CD-ROM, floppy disk (not illustrated), tape drive (not illustrated), ZIP® drive (not illustrated) or through the network. Next the user causes execution of expression profile analysis software which performs the methods of the present invention.

[0210] In another exemplary implementation, a user first loads experimental data and/or databases into the computer system. This data is loaded into the memory from the storage media or from a remote computer, preferably from a dynamic geneset database system, through the network. Next the user causes execution of software that performs the steps of the present invention.

[0211] Additionally, because the data obtained and analyzed in the software and computer system products of the invention are confidential, the software and/or computer system comprises access controls or access control routines, such as Alternative computer systems and software for implementing the analytic methods of this invention will be apparent to one of skill in the art and are intended to be comprehended within the accompanying claims. In particular, the accompanying claims are intended to include the alternative program structures for implementing the methods of this invention that will be readily apparent to one of skill in the art.

[0212] 6. EXAMPLES

[0213] Materials And Methods

[0214] 117 tumor samples from breast cancer patients were collected. RNA samples were then prepared, and each RNA sample was profiled using inkjet-printed microarrays. Marker genes were then identified based on expression patterns; these genes were then used to train classifiers, which used these marker genes to classify tumors into diagnostic and prognostic categories. Finally, these marker genes were used to predict the diagnostic and prognostic outcome for a group of individuals.

[0215] 1. Sample Collection

[0216] 117 breast cancer patients treated at The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands, were selected on the basis of the following clinical criteria (data extracted from the medical records of the NKI/AvL Tumor Register, Biometrics Department).

[0217] Group 1 (n=97, 78 for training, 19 for independent tests) was selected on the basis of: (1) primary invasive breast carcinoma <5 cm (T1 or T2); (2) no axillary metastases (NO); (3) age at diagnosis <55 years; (4) calender year of diagnosis 1983-1996; and (5) no prior malignancies (excluding carcinoma in situ of the cervix or basal cell carcinoma of the skin). All patients were treated by modified radical mastectomy (n=34) or breast conserving treatment (n=64), including axillary lymph node dissection. Breast conserving treatment consisted of excision of the tumor, followed by radiation of the whole breast to a dosis of 50 Gy, followed by a boost varying from 15 to 25 Gy. Five patients received adjuvant systemic therapy consisting of chemotherapy (n=3) or hormonal therapy (n=2), all other patients did not receive additional treatment. All patients were followed at least annually for a period of at least 5 years. Patient follow-up information was extracted from the Tumor Registry of the Biometrics Department.

[0218] Group 2 (n=20) was selected as: (1) carriers of a germline mutation in BRCA1 or BRCA2; and (2) having primary invasive breast carcinoma. No selection or exclusion was made based on tumor size, lymph node status, age at diagnosis, calender year of diagnosis, other malignancies. Germline mutation status was known prior to this research protocol.

[0219] Information about individual from which tumor samples were collected include: year of birth; sex; whether the individual is pre- or post-menopausal; the year of diagnosis; the number of positive lymph nodes and the total number of nodes; whether there was surgery, and if so, whether the surgery was breast-conserving or radical; whether there was radiotherapy, chemotherapy or hormonal therapy. The tumor was graded according to the formula P=TNM, where T is the tumor size (on a scale of 0-5); N is the number of nodes that are positive (on a scale of 0-4); and M is metastases (0=absent, 1=present). The tumor was also classified according to stage, tumor type (in situ or invasive; lobular or ductal; grade) and the presence or absence of the estrogen and progesterone receptors. The progression of the cancer was described by (where applicable): distant metastases; year of distant metastases, year of death, year of last follow-up; and BRCA1 genotype.

[0220] 2. Tumors:

[0221] Germline mutation testing of BRCA1 and BRCA2 on DNA isolated from peripheral blood lymphocytes includes mutation screening by a Protein Truncation Test (PTT) of exon 11 of BRCA1 and exon 10 and 11 of BRCA2, deletion PCR of BRCA1 genomic deletion of exon 13 and 22, as well Denaturing Gradient Gel Electrophoresis (DGGE) of the remaining exons. Aberrant bands were all confirmed by genomic sequencing analyzed on a ABI3700 automatic sequencer and confirmed on a independent DNA sample.

[0222] From all, tumor material was snap frozen in liquid nitrogen within one hour after surgery. Of the frozen tumor material an H&E (hematoxylin-eosin) stained section was repared prior to and after cutting slides for RNA isolation. These H&E frozen sections ere assessed for the percentage of tumor cells; only samples with >50% tumor cells were selected for further study.

[0223] For all tumors, surgical specimens fixed in formaldehyde and embedded in paraffin were evaluated according to standard histopathological procedures. H&E stained paraffin sections were examined to assess tumor type (e.g., ductal or lobular according to the WHO classification); to assess histologic grade according the method described by Elston and Ellis (grade 1-3); and to assess the presence of lymphangio-invasive growth and the presence of an extensive lymphocytic infiltrate. All histologic factors were independently assessed by two pathologists (MV and JL); consensus on differences was reached by examining the slides together. A representative slide of each tumor was used for immunohistochemical staining with antibodies directed against the estrogen- and progesterone receptor by standard procedures. The staining result was scored as the percentage of positively staining nuclei (0%, 10%, 20%, etc., up to 100%).

[0224] 3. Amplification, Labeling, and Hybridization

[0225] The outline for the production of marker-derived nucleic acids and hybridization of the nucleic acids to a microarray are outlined in FIG. 2. 30 frozen sections of 30 μM thickness were used for total RNA isolation of each snap frozen tumor specimen. Total RNA was isolated with RNAzol™ B (Campro Scientific, Veenendaal, The Netherlands) according to the manufacturers protocol, including homogenization of the tissue using a Polytron PT-MR2100 (Merck, Amsterdam, The Netherlands) and finally dissolved in RNAse-free H₂O. The quality of the total RNA was assessed by A260/A280 ratio and had to be between 1.7 and 2.1 as well as visual inspection of the RNA on an agarose gel which should indicate a stronger 28S ribosomal RNA band compared to the 18S ribosomal RNA band, subsequently, 25 μg of total RNA was DNase treated using the Qiagen RNASE-free DNase kit and RNeasy spin columns (Qiagen Inc, GmbH, Germany) according to the manufacturers protocol. DNase treated total RNA was dissolved in RNASE-free H₂O to a final concentration of 0.2 μg/μl.

[0226] 5 μg total RNA was used as input for cRNA synthesis. An oligo-dT primer containing a T7 RNA polymerase promoter sequence was used to prime first strand cDNA synthesis, and random primers (pdN6) were used to prime second strand cDNA synthesis by MMLV reverse transcriptase. This reaction yielded a double-stranded cDNA that contained the T7 RNA polymerase (T7RNAP) promoter. The double-stranded cDNA was then transcribed into cRNA by T7RNAP.

[0227] cRNA was labeled with Cy3 or Cy5 dyes using a two-step process. First, allylamine-derivatized nucleotides were enzymatically incorporated into cRNA products. For cRNA labeling, a 3:1 mixture of 5-(3-Aminoallyl)uridine 5′-triphosphate (Sigma) and UTP was substituted for UTP in the in vitro transcription (IVT) reaction. Allylamine-derivatized cRNA products were then reacted with N-hydroxy succinimide esters of Cy3 or Cy5 (CyDye, Amersham Pharmacia Biotech). 5 μg Cy5-labeled cRNA from one breast cancer patient was mixed with the same amount of Cy3-labeled product from a pool of equal amount of cRNA from each individual sporadic patient.

[0228] Microarray hybridizations were done in duplicate with fluor reversals. Before hybridization, labeled cRNAs were fragmented to an average size of 50-100nt by heating at 60° C. in the presence of 10 mM ZnCl2. Fragmented cRNAs were added to hybridization buffer containing 1 M NaCl, 0.5% sodium sarcosine and 50 mM MES, pH 6.5, which stringency was regulated by the addition of formamide to a final concentration of 30%. Hybridizations were carried out in a final volume of 3 ml at 40° C. on a rotating platform in a hybridization oven (Robbins Scientific) for 48 h. After hybridization, slides were washed and scanned using a confocal laser scanner (Agilent Technologies). Fluorescence intensities on scanned images were quantified, normalized and corrected.

[0229] 4. Pooling of Samples

[0230] The reference cRNA pool was formed by pooling equal amount of cRNAs from each individual sporadic patient, for a total of 78 tumors.

[0231] 5. 25 k Human Microarray

[0232] Surface-bound oligonucleotides were synthesized essentially as proposed by Blanchard et al., Biosens. Bioelectron. 6(7):687-690 (1996); see also Hughes et al., Nature Biotech. 19(4):342-347 (2000). Hydrophobic glass surfaces (3 inches by 3 inches) containing exposed hydroxyl groups were used as substrates for nucleotide synthesis. Phosphoramidite monomers were delivered to computer-defined positions on the glass surfaces using ink-jet printer heads. Unreacted monomers were then washed away and the ends of the extended oligonucleotides were deprotected. This cycle of monomer coupling, washing and deprotection was repeated for each desired layer of nucleotide synthesis. Oligonucleotide sequences to be printed were specified by computer files.

[0233] Microarrays containing approximately 25,000 human gene sequences (Hu25K microarrays) were used for this study. Sequences for microarrays were selected from RefSeq (a collection of non-redundant mRNA sequences, located on the Internet at nlm.nih.gov/LocusLink/refseq.html) and Phil Green EST contigs, which is a collection of EST contigs assembled by Dr. Phil Green et al at the University of Washington (Ewing and Green, Nat. Genet. 25(2):232-4 (2000)), available on the Internet at phrap.org/est_assembly/index.html. Each mRNA or EST contig was represented on Hu25K microarray by a single 60mer oligonucleotide essentially as described in Hughes et al., Nature Biotech. 19(4):342-347 and in International Publication WO 01/06013, published Jan. 25, 2001, and in International Publication WO 01/05935, published Jan. 25, 2001, except that the rules for oligo screening were modified to remove oligonucleotides with more than 30% C or with 6 or more contiguous C residues.

Example 1 Differentially Regulated Gene Sets and Overall Expression Patterns of Breast Cancer Tumors

[0234] Of the approximately 25,000 sequences represented on the microarray, a group of approximately 5,000 genes that were significantly regulated across the group of samples was selected. A gene was determined to be significantly differentially regulated with cancer of the breast if it showed more than two-fold of transcript changes as compared to a sporadic tumor pool, and if the p-value for differential regulation (Hughes et al., Cell 102:109-126 (2000)) was less than 0.01 either upwards or downwards in at least five out of 98 tumor samples.

[0235] An unsupervised clustering algorithm allowed us to cluster patients based on their similarities measured over this set of −5,000 significant genes. The similarity between two patients x and y is defined as $\begin{matrix} \begin{matrix} {S = {1 - \left\lbrack {\sum\limits_{i = 1}^{N_{V}}{\frac{\left( {x_{i} - \overset{\_}{x}} \right)}{\sigma_{x_{i}}}{\frac{\left( {y_{i} - \overset{\_}{y}} \right)}{\sigma_{y_{i}}}/}}} \right.}} \\ \left. \sqrt{\sum\limits_{i = 1}^{N_{V}}{\left( \frac{x_{i} - \overset{\_}{x}}{\sigma_{x_{i}}} \right)^{2}{\sum\limits_{i = 1}^{N_{V}}\left( \frac{y_{i} - \overset{\_}{y}}{\sigma_{y_{i}}} \right)^{2}}}} \right\rbrack \end{matrix} & {{Equation}\quad (5)} \end{matrix}$

[0236] In Equation (5), X and Y are two patients with components of log ratio x_(i) and y_(i), i=1, . . . , N=5,100. Associated with every value x_(i) is error σ_(x) _(i) . The smaller the value σ_(x) _(i) , the more reliable the measurement $\overset{\_}{x} = {\sum\limits_{i = 1}^{N_{V}}{\frac{x_{i}}{\sigma_{x_{i}}^{2}}/{\sum\limits_{i = 1}^{N_{V}}\frac{1}{\sigma_{x_{i}}^{2}}}}}$

[0237] is the error-weighted arithmetic mean. The use of correlation as similarity metric emphasizes the importance of co-regulation in clustering rather than the amplitude of regulations.

[0238] The set of approximately 5,000 genes can be clustered based on their similarities measured over the group of 98 tumor samples. The similarity between two genes was defined in the same way as in Equation (1) except that now for each gene, there are 98 components of log ratio measurements.

[0239] The result of such a two-dimensional clustering is displayed in FIG. 3. Two distinctive patterns emerge from the clustering. The first pattern consists of a group of patients in the lower part of the plot whose regulations are very different from the sporadic pool. The other pattern is made of a group of patients in the upper part of the plot whose expressions are only moderately regulated in comparison with the sporadic pool. These dominant patterns suggest that the tumors can be unambiguously divided into two distinct types based on this set of 5,000 significant genes.

[0240] To help understand these patterns, they were associated with estrogen-receptor (ER), proestrogen receptor (PR), tumor grade, presence of lymphocytic infiltrate, 2 and angioinvasion (FIG. 3). The lower group in FIG. 3, which features the dominant pattern, consists of 36 patients. Of the 39 ER-negative patients, 34 patients are clustered together in this group. From FIG. 4, it was observed that the expression of estrogen receptor alpha gene ESR1 and a large group of co-regulated genes are consistent with this expression pattern.

[0241] From FIG. 3 and FIG. 4, it was concluded that gene expression patterns can be used to classify tumor samples into subgroups of diagnostic interest. Thus, genes co-regulated across 98 tumor samples contain information about the molecular basis of breast cancers. The combination of clinical data and microarray measured gene abundance of ESR1 demonstrates that the distinct types are related to, or at least are reported by, the ER status.

Example 2 Identification of Genetic Markers Distinguishing Estrogen Receptor (+) From Estrogen Receptor (−) Patients

[0242] The results described in this Example allow the identification of expression marker genes that differentiate two major types of tumor cells: “ER-negative” group and “ER-positive” group. The differentiation of samples by ER(+) status was accomplished in our steps: (1) identification of a set of candidate marker genes that correlate with ER level; 2) rank-ordering these candidate genes by strength of correlation; (3) optimization of the umber of marker genes; and (4) classifying samples based on these marker genes.

[0243] 1. Selection of Candidate Discriminating Genes

[0244] In the first step, a set of candidate discriminating genes was identified based on gene expression data of training samples. Specifically, we calculated the correlation coefficients ρ between the category numbers or ER level and logarithmic expression ratio {right arrow over (r)} across all the samples for each individual gene:

ρ=({right arrow over (c)}•{right arrow over (r)})/(∥{right arrow over (c)}∥·∥{right arrow over (r)}∥)  Equation (2)

[0245] The histogram of resultant correlation coefficients is shown in FIG. 5A as a gray line. While the amplitude of correlation or anti-correlation is small for the majority of genes, the amplitude for some genes is as great as 0.5. Genes whose expression ratios either correlate or anti-correlate well with the diagnostic category of interest are used as reporter genes for the category.

[0246] Genes having a correlation coefficient larger than 0.3 (“correlated genes”) or less than −0.3 (“anti-correlated genes”) were selected as reporter genes. The threshold of 0.3 was selected based on the correlation distribution for cases where there is no real correlation (one can use permutations to determine this distribution). Statistically, this distribution width depends upon the number of samples used in the correlation calculation. The distribution width for control cases (no real correlation) is approximately 1/{square root}{square root over (n−3)}, where n=the number of samples. In our case, n=98. Therefore, a threshold of 0.3 roughly corresponds to 3−σ in the distribution (3×1/{square root}{square root over (n−3)}).

[0247] 2,460 such genes were found to satisfy this criterion. In order to evaluate the significance of the correlation coefficient of each gene with the ER level, a bootstrap technique was used to generate Monte-Carlo data that randomize the association between gene expression data of the samples and their categories. The distribution of correlation coefficients obtained from one Monte-Carlo trial is shown as a dashed line in FIG. 5A. To estimate the significance of the 2,460 marker genes as a group, 10,000 Monte-Carlo runs were generated. The collection of 10,000 such Monte-Carlo trials forms the null hypothesis. The number of genes that satisfy the same criterion for Monte-Carlo data varies from run to run. The frequency distribution from 10,000 Monte-Carlo runs of the number of genes having correlation coefficients of >0.3 or <-0.3 is displayed in FIG. 5B. Both the mean and maximum value are much smaller than 2,460. Therefore, the significance of this gene group as the discriminating gene set between ER(+) and ER(−) samples is estimated to be greater than 99.99%.

[0248] 2. Rank-Ordering of Candidate Discriminating Genes

[0249] In the second step, genes on the candidate list were rank-ordered based on the significance of each gene as a discriminating gene. The markers were rank-ordered either by amplitude of correlation, or by using a metric similar to a Fisher statistic:

t=(

x ₁

−

x ₂

)/{square root}{square root over ([σ₁ ²(n ₁−1)+σ₂ ²(n ₂−1)]/(n ₁ +n ₂−1)/(1/n ₁+1/n ₂))}  Equation (3)

[0250] In Equation (3),

x₁

is the error-weighted average of log ratio within the ER(−), and

x₂

is the error-weighted average of log ratio within the ER(+) group. σ₁ is the variance of log ratio within the ER(−) group and n₁ is the number of samples that had valid measurements of log ratios. σ₂ is the variance of log ratio within the ER(+) group and n₂ is the number of samples that had valid measurements of log ratios. The t-value in Equation (3) represents the variance-compensated difference between two means. The confidence level of each gene in the candidate list was estimated with respect to a null hypothesis derived from the actual data set using a bootstrap technique; that is, many artificial data sets were generated by randomizing the association between the clinical data and the gene expression data.

[0251] 3. Optimization of the Number of Marker Genes

[0252] The leave-one-out method was used for cross validation in order to optimize the discriminating genes. For a set of marker genes from the rank-ordered candidate list, a classifier was trained with 97 samples, and was used to predict the status of the remaining sample. The procedure was repeated for each of the samples in the pool, and the number of cases where the prediction for the one left out is wrong or correct was counted.

[0253] The above performance evaluation from leave-one-out cross validation was repeated by successively adding more marker genes from the candidate list. The performance as a function of the number of marker genes is shown in FIG. 6. The error rates for type 1 and type 2 errors varied with the number of marker genes used, but were both minimal while the number of the marker genes is around 550. Therefore, we consider this set of 550 genes is considered the optimal set of marker genes that can be used to classify breast cancer tumors into “ER-negative” group and “ER-positive” group. FIG. 7 shows the classification of patients as ER(+) or ER(−) based on this 550 marker set. FIG. 8 shows the correlation of each tumor to the ER-negative template versus the correlation of each tumor to the ER-positive template.

[0254] 4. Classification Based on Marker Genes

[0255] In the third step, a set of classifier parameters was calculated for each type of training data set based on either of the above ranking methods. A template for the ER(−) group ({right arrow over (z)}₁) was generated using the error-weighted log ratio average of the selected group of genes. Similarly, a template for ER(+) group (called {right arrow over (z)}₂) was generated using the error-weighted log ratio average of the selected group of genes. Two classifier parameters (P₁ and P₂) were defined based on either correlation or distance. P measures the similarity between one sample {right arrow over (y)} and the ER(−) template {right arrow over (z)}₁ over this selected group of genes. P₂ measures the similarity between one sample {right arrow over (y)} and the ER(+) template {right arrow over (z)}₂ over this selected group of genes. The correlation P_(i) is defined as:

P _(i)=({right arrow over (z)} _(i•{right arrow over (y)})/(∥) {right arrow over (z)} _(i) ∥·∥{right arrow over (y)}∥)  Equation (1)

[0256] A “leave-one-out” method was used to cross-validate the classifier built based on the marker genes. In this method, one sample was reserved for cross validation each time the classifier was trained. For the set of 550 optimal marker genes, the classifier was trained with 97 of the 98 samples, and the status of the remaining sample was predicted. This procedure was performed with each of the 98 patients. The number of cases where the prediction was wrong or correct was counted. It was further determined that subsets of as few as 50 of the 2,460 genes are able classify tumors as ER(+) or ER(−) nearly as well as using the total set.

[0257] In a small number of cases, there was disagreement between classification by the 550 marker set and a clinical classification. In comparing the microarray measured log ratio of expression for ESR1 to the clinical binary decision (negative or positive) of ER status for each patient, it was seen that the measured expression is consistent with the qualitative category of clinical measurements (mixture of two methods) for the majority of tumors. For example, two patients who were clinically diagnosed as ER(+) actually exhibited low expression of ESR1 from microarray measurements and were classified as ER negative by 550 marker genes. Additionally, 3 patients who were clinically diagnosed as ER(−) exhibited high expression of ESR1 from microarray measurements and were classified as ER(+) by the same 550 marker genes. Statistically, however, microarray measured gene expression of ESR 1 correlates with the dominant pattens better than clinically determined ER status.

Example 3 Identification of Genetic Markers Distinguishing BRCA1 Tumors From Sporadic Tumors in Estrogen Receptor (−) Patients

[0258] The BRCA1 mutation is one of the major clinical categories in breast cancer tumors. It was determined that of tumors of 38 patients in the ER(−) group, 17 exhibited the BRCA1 mutation, while 21 were sporadic tumors. A method was therefore developed that enabled the differentiation of the 17 BRCA1 mutation tumors from the 21 sporadic tumors in the ER(−) group.

[0259] 1. Selection of candidate discriminating genes In the first step, a set of candidate genes was identified based on the gene expression patterns of these 38 samples. We first calculated the correlation between the BRCA1-mutation category number and the expression ratio across all 38 samples for each individual gene by Equation (2). The distribution of the correlation coefficients is shown as a histogram defined by the solid line in FIG. 9A. We observed that, while the majority of genes do not correlate with BRCA1 mutation status, a small group of genes correlated at significant levels. It is likely that genes with larger correlation coefficients would serve as reporters for discriminating tumors of BRCA1 mutation carriers from sporadic tumors within the ER(−) group.

[0260] In order to evaluate the significance of each correlation coefficient with respect to a null hypothesis that such correlation coefficient could be found by chance, a bootstrap technique was used to generate Monte-Carlo data that randomizes the association between gene expression data of the samples and their categories. 10,000 such Monte-Carlo runs were generated as a control in order to estimate the significance of the marker genes as a group. A threshold of 0.35 in the absolute amplitude of correlation coefficients (either correlation or anti-correlation) was applied both to the real data and the Monte-Carlo data. Following this method, 430 genes were found to satisfy this criterion for the experimental data. The p-value of the significance, as measured against the 10,000 Monte-Carlo trials, is approximately 0.0048 (FIG. 9B). That is, the probability that this set of 430 genes contained useful information about BRCA1-like tumors vs sporadic tumors exceeds 99%.

[0261] 2. Rank-Ordering of Candidate Discriminating Genes

[0262] In the second step, genes on the candidate list were rank-ordered based on the significance of each gene as a discriminating gene. Here, we used the absolute amplitude of correlation coefficients to rank order the marker genes.

[0263] 3 Optimization of Discriminating Genes

[0264] In the third step, a subset of genes from the top of this rank-ordered list was used for classification. We defined a BRCA1 group template (called {right arrow over (z)}₁) by using the error-weighted log ratio average of the selected group of genes. Similarly, we defined a non-BRCA1 group template (called {right arrow over (z)}₂) by using the error-weighted log ratio average of the selected group of genes. Two classifier parameters (P1 and P2) were defined based on either correlation or distance. P1 measures the similarity between one sample Y and the BRCA1 template {right arrow over (z)}₁ over this selected group of genes. P2 measures the similarity between one sample {right arrow over (y)} and the non-BRCA1 template {right arrow over (z)}₂ over this selected group of genes. For correlation, P1 and P2 were defined in the same way as in Equation (4).

[0265] The leave-one-out method was used for cross validation in order to optimize the discriminating genes as described in Example 2. For a set of marker genes from the rank-ordered candidate list, the classifier was trained with 37 samples the remaining one was predicted. The procedure was repeated for all the samples in the pool, and the number of cases where the prediction for the one left out is wrong or correct was counted.

[0266] To determine the number of markers constituting a viable subset, the above performance evaluation from leave-one-out cross validation was repeated by cumulatively adding more marker genes from the candidate list. The performance as a function of the umber of marker genes is shown in FIG. 10. The error rates for type 1 (false negative) and type 2 (false positive) errors (Bendat & Piersol, RANDOM DATA ANALYSIS AND MEASUREMENT PROCEDURES, 2D ED., Wiley Interscience, p. 89) reached optimal ranges when the number of the marker genes is approximately 100. Therefore, a set of about 100 genes is considered to be the optimal set of marker genes that can be used to classify tumors in the ER(−) group as either BRCA1-related tumors or sporadic tumors.

[0267] The classification results using the optimal 100 genes are shown in FIGS. 11A and 11B. As shown in FIG. 11A, the co-regulation patterns of the sporadic patients differ from those of the BRCA1 patients primarily in the amplitude of regulation. Only one sporadic tumor was classified into the BRCA1 group. Patients in the sporadic group are not necessarily BRCA1 mutation negative; however, it is estimated that only approximately 5% of sporadic tumors are indeed BRCA1-mutation carriers.

Example 4 Identification of Genetic Markers Distinguishing Sporadic Tumor Patients with >5 Year Versus <5 Year Survival Times

[0268] 78 tumors from sporadic breast cancer patients were used to explore prognostic predictors from gene expression data. Of the 78 samples in this sporadic breast cancer group, 44 samples were known clinically to have had no distant metastases within 5 years since the initial diagnosis (“no distant metastases group”) and 34 samples had distant metastases within 5 years since the initial diagnosis (“distant metastases group”). A group of 231 markers, and optimally a group of 70 markers, was identified that allowed differentiation between these two groups.

[0269] 1. Selection of Candidate Discriminating Genes

[0270] In the first step, a set of candidate discriminating genes was identified based on gene expression data of these 78 samples. The correlation between the prognostic category number (distant metastases vs no distant metastases) and the logarithmic expression ratio across all samples for each individual gene was calculated using Equation (2). The distribution of the correlation coefficients is shown as a solid line in FIG. 12A. FIG. 12A also shows the result of one Monte-Carlo run as a dashed line. We observe that even though the majority of genes do not correlate with the prognostic categories, a small group of genes do correlate. It is likely that genes with larger correlation coefficients would be more useful as reporters for the prognosis of interest—distant metastases group and no distant metastases group.

[0271] In order to evaluate the significance of each correlation coefficient with respect to a null hypothesis that such correlation coefficient can be found by chance, we used a bootstrap technique to generate data from 10,000 Monte-Carlo runs as a control (FIG. 12B). We then selected genes that either have the correlation coefficient larger than 0.3 (“correlated genes”) or less than −0.3 (“anti-correlated genes”). The same selection criterion was applied both to the real data and the Monte-Carlo data. Using this comparison, 231 markers from the experimental data were identified that satisfy this criterion. The probability of this gene set for discriminating patients between the distant metastases group and the no distant metastases group being chosen by random fluctuation is approximately 0.003.

[0272] 2. Rank-Ordering of Candidate Discriminating Genes

[0273] In the second step, genes on the candidate list were rank-ordered based on the significance of each gene as a discriminating gene. Specifically, a metric similar to a “Fisher” statistic, defined in Equation (3), was used for the purpose of rank ordering. The confidence level of each gene in the candidate list was estimated with respect to a null hypothesis derived from the actual data set using the bootstrap technique. Genes in the candidate list can also be ranked by the amplitude of correlation coefficients.

[0274] 3. Optimization of Discriminating Genes

[0275] In the third step, a subset of 5 genes from the top of this rank-ordered list was selected to use as discriminating genes to classify 78 tumors into a “distant metastases group” or a “no distant metastases group”. The leave-one-out method was used for cross validation. Specifically, 77 samples defined a classifier based on the set of selected discriminating genes, and these were used to predict the remaining sample. This procedure was repeated so that each of the 78 samples was predicted. The number of cases in which predictions were correct or incorrect were counted. The performance of the classifier was measured by the error rates of type 1 and type 2 for this selected gene set.

[0276] We repeated the above performance evaluation procedure, adding 5 more marker genes each time from the top of the candidate list, until all 231 genes were used. As shown in FIG. 13, the number of mis-predictions of type 1 and type 2 errors change dramatically with the number of marker genes employed. The combined error rate reached a minimum when 70 marker genes from the top of our candidate list were used. Therefore, this set of 70 genes is the optimal, preferred set of marker genes useful for the classification of sporadic tumor patients into either the distant metastases or no distant metastases group. Fewer or more markers also act as predictors, but are less efficient, either because of higher error rates, or the introduction of statistical noise.

[0277] 4. Reoccurrence Probability Curves

[0278] The prognostic classification of 78 patients with sporadic breast cancer tumors into two distinct subgroups was predicted based on their expression of the 70 optimal marker genes (FIGS. 14 and 15).

[0279] To evaluate the prognostic classification of sporadic patients, we predicted the outcome of each patient by a classifier trained by the remaining 77 patients based on the 70 optimal marker genes. FIG. 16 plots the distant metastases probability as a function of the time since initial diagnosis for the two predicted groups. The difference between these two reoccurrence curves is significant. Using the %² test (S-PLUS 2000 Guide to Statistics, vol. 2, MathSoft, p. 44), the p-value is estimated to be 10-9. The distant metastases probability as a function of the time since initial diagnosis was also compared between ER(+) and ER(−) individuals (FIG. 17), PR(+) and PR(−) individuals (FIG. 18), and between individuals with different tumor grades (FIGS. 19A, 19B). In comparison, the p-values for the differences between two prognostic groups based on clinical data are much less significant than that based on gene expression data, ranging from 10⁻³ to 1.

[0280] To parameterize the reoccurrence probability as a function of time since initial diagnosis, the curve was fitted to one type of survival model—“normal”:

P=α×exp(−t ²/τ²)  (4)

[0281] For fixed α=1, we found that τ=125 months for patients in the no distant metastases group and τ=36 months for patients in the distant metastases group. Using tumor grades, we found τ=100 months for patients with tumor grades 1 and 2 and τ=60 for patients with tumor grade 3. It is accepted clinical practice that tumor grades are the best available prognostic predictor. However, the difference between the two prognostic groups classified based on 70 marker genes is much more significant than those classified by the best available clinical information.

[0282] 5. Prognostic Prediction for 19 Independent Sporadic Tumors

[0283] To confirm the proposed prognostic classification method and to ensure the reproducibility, robustness, and predicting power of the 70 optimal prognostic marker genes, we applied the same classifier to 19 independent tumor samples from sporadic breast cancer patients, prepared separately at The Netherlands Cancer Institute (NKI). The same reference pool was used.

[0284] The classification results of 19 independent sporadic tumors are shown in FIG. 20. FIG. 20A shows the log ratio of expression regulation of the same 70 optimum marker genes. Based on our classifier model, we expected the misclassification of 19*(6+7)/78=3.2 tumors. Consistently, (1+3)=4 of 19 tumors were misclassified.

[0285] 6. Clinical Parameters as a Group vs. Microarray Data—Results of Logistic Regression

[0286] In the previous section, the predictive power of each individual clinical parameter was compared with that of the expression data. However, it is more meaningful to combine all the clinical parameters as a group, and then compare them to the expression data. This requires multi-variant modeling; the method chosen was logistic regression. Such an approach also demonstrates how much improvement the microarray approach adds to the results of the clinical data.

[0287] The clinical parameters used for the multi-variant modeling were: (1) tumor grade; (2) ER status; (3) presence or absence of the progestogen receptor (PR); (4) tumor size; (5) patient age; and (6) presence or absence of angioinvasion. For the microarray data, two correlation coefficients were used. One is the correlation to the mean of the good prognosis group (C1) and the other is the correlation to the mean of the bad prognosis group (C2). When calculating the correlation coefficients for a given patient, this patient is excluded from either of the two means.

[0288] The logistic regression optimizes the coefficient of each input parameter to best predict the outcome of each patient. One way to judge the predictive power of each input parameter is by how much deviance (similar to Chi-square in the linear regression, see for example, Hasomer & Lemeshow, APPLIED LOGISTIC REGRESSION, John Wiley & Sons, (2000)) the parameter accounts for. The best predictor should account for most of the deviance. To fairly assess the predictive power, each parameter was modeled independently. The microarray parameters explain most of the deviance, and hence are powerful predictors.

[0289] The clinical parameters, and the two microarray parameters, were then monitored as a group. The total deviance explained by the six clinical parameters was 31.5, and total deviance explained by the microarray parameters was 39.4. However, when the clinical data was modeled first, and the two microarray parameters added, the final deviance accounted for is 57.0.

[0290] The logistic regression computes the likelihood that a patient belongs to the good or poor prognostic group. FIGS. 21A and 21B show the sensitivity vs. (1-specificity). The plots were generated by varying the threshold on the model predicted likelihood. The curve which goes through the top left corner is the best (high sensitivity with high specificity). The microarray outperformed the clinical data by a large margin. For example, at a fixed sensitivity of around 80%, the specificity was ˜80% from the microarray data, and ˜65% from the clinical data for the good prognosis group. For the poor prognosis group, the corresponding specificities were ˜80% and ˜70%, again at a fixed sensitivity of 80%. Combining the microarray data with the clinical data further improved the results. The result can also be displayed as the total error rate as the function of the threshold in FIG. 21C. At all possible thresholds, the error rate from the microarray was always smaller than that from the clinical data. By adding the microarray data to the clinical data, the error rate is further reduced, as one can see in FIG. 21C.

[0291] Odds ratio tables can be created from the prediction of the logistic regression. The probability of a patient being in the good prognosis group is calculated by the logistic regression based on different combinations of input parameters (clinical and/or microarray). Patients are divided into the following four groups according to the prediction and the true outcome: (1) predicted good and truly good, (2) predicted good but truly poor, (3) predicted poor but truly good, (4) predicted poor and truly poor. Groups (1) & (4) represent correct predictions, while groups (2) & (3) represent mis-predictions. The division for the prediction is set at probability of 50%, although other thresholds can be used. The results are listed in Table 8. It is clear from Table 8 that microarray profiling (Table 8.3 & 8.10) outperforms any single clinical data (Table 8.4-8.9) and the combination of the clinical data (Table 8.2). Adding the micro-array profiling in addition to the clinical data give the best results (Table 8.1).

[0292] For microarray profiling, one can also make a similar table (Table 8.11) without using logistic regression. In this case, the prediction was simply based on C₁-C₂ (greater than 0 means good prognosis, less than 0 mean poor prognosis). TABLE 8.1 Prediction by clinical + microarray Predicted good Predicted poor true good 39 5 true poor 4 30

[0293] TABLE 8.2 Prediction by clinical alone Predicted good Predicted poor true good 34 10 true poor 12 22

[0294] TABLE 8.3 Prediction by microarray predicted good Predicted poor true good 39 5 true poor 10 24

[0295] TABLE 8.4 Prediction by grade Predicted good Predicted poor true good 23 21 true poor 5 29

[0296] TABLE 8.5 Prediction by ER Predicted good Predicted poor true good 35 9 true poor 21 13

[0297] TABLE 8.6 Prediction by PR Predicted good Predicted poor true good 35 9 true poor 18 16

[0298] TABLE 8.7 Prediction by size Predicted good Predicted poor true good 35 9 true poor 13 21

[0299] TABLE 8.8 Prediction by age Predicted good Predicted poor true good 33 11 true poor 15 19

[0300] TABLE 8.9 Prediction by angioinvasion Predicted good Predicted poor true good 37 7 true poor 19 15

[0301] TABLE 8.10 Prediction by dC (C1-C2) Predicted good Predicted poor true good 36 8 true poor 6 28

[0302] TABLE 8.11 No logistic regression, simply judged by C1-C2 Predicted good Predicted poor true good 37 7 true poor 6 28

Example 5 Concept of Mini-Array for Diagnosis Purposes

[0303] All genes on the marker gene list for the purpose of diagnosis and prognosis can be synthesized on a small-scale microarray using ink-jet technology. A microarray with genes for diagnosis and prognosis can respectively or collectively be made. Each gene on the list is represented by single or multiple oligonucleotide probes, depending on its sequence uniqueness across the genome. This custom designed mini-array, in combination with sample preparation protocol, can be used as a diagnostic/prognostic kit in clinics.

Example 6 Biological Significance of Diagnostic Marker Genes

[0304] The public domain was searched for the available functional annotations for the 430 marker genes for BRCA1 diagnosis in Table 3. The 430 diagnostic genes in Table 3 can be divided into two groups: (1) 196 genes whose expressions are highly expressed in BRCA1-like group; and (2) 234 genes whose expression are highly expressed sporadic group. Of the 196 BRCA1 group genes, 94 are annotated. Of the 234 sporadic group genes, 100 are annotated. The terms “T-cell”, “B-cell” or “immunoglobulin” are involved in 13 of the 94 annotated genes, and in 1 of the 100 annotated genes, respectively. Of 24,479 genes represented on the microarrays, there are 7,586 genes with annotations to date. “T-cell”, B-cell” and “immunoglobulin” are found in 207 of these 7,586 genes. Given this, the p-value of the 13 “T-cell”, “B-cell” or “immunoglobulin” genes in the BRCA1 group is very significant (p-value =1.1×10⁻6). In comparison, the observation of 1 gene relating to “T-cell”, “B-cell”, or “immunoglobulin” in the sporadic group is not significant (p-value 0.18).

[0305] The observation that BRCA1 patients have highly expressed lymphocyte (T-cell and B-cell) genes agrees with what has been seen from pathology that BRCA1 breast tumor has more frequently associated with high lymphocytic infiltration than sporadic cases (Chappuis et al., 2000, Semin Surg Oncol 18:287-295).

Example 7 Biological Significance of Prognosis Marker Genes

[0306] A search was performed for available functional annotations for the 231 prognosis marker genes (Table 5). The markers fall into two groups: (1) 156 markers whose expressions are highly expressed in poor prognostic group; and (2) 75 genes whose expression are highly expressed in good prognostic group. Of the 156 markers, 72 genes are annotated; of the 75 genes, 28 genes are annotated.

[0307] Twelve of the 72 markers, but none of the 28 markers, are, or are associated with, kinases. In contrast, of the 7,586 genes on the microarray having annotations to date, only 471 involve kinases. On this basis, the p-value that twelve kinase-related markers in the poor prognostic group is significant (p-value =0.001). Kinases are important regulators of intracellular signal transduction pathways mediating cell proliferation, differentiation and apoptosis. Their activity is normally tightly controlled and regulated. Overexpression of certain kinases is well known involving in oncogenesis, such as vascular endothelial growth factor receptor1 (VEGFR1 or FLT1), a tyrosine kinase in the poor prognosis group, which lays a very important role in tumor angiogenesis. Interestingly, vascular endothelial growth factor (VEGF), VEGFR's ligand, is also found in the prognosis group, which means both ligand and receptor are upregulated in poor prognostic individuals by an unknown mechanism.

[0308] Likewise, 16 of the 72 markers, and only two of the 28 markers, are, or are associated with, ATP-binding or GTP-binding proteins. In contrast, of the 7,586 genes on the microarray having annotations to date, only 714 and 153 involve ATP-binding and GTP-binding, respectively. On this basis, the p-value that 16 GTP- or ATP-binding-related markers in the poor prognosis group is significant (p-value 0.001 and 0.0038). Thus, the kinase- and ATP— or GTP-binding-related markers within the 72 markers can be used as prognostic indicators.

[0309] Cancer is characterized by deregulated cell proliferation. On the simplest level, this requires division of the cell or mitosis. By keyword searching, we found “cell division” or “mitosis” included in the annotations of 7 genes respectively in the 72 annotated markers from the 156 poor prognosis markers, but in none for the 28 annotated genes from 75 good prognosis markers. Of the 7,586 microarray markers with annotations, “cell division” is found in 62 annotations and “mitosis” is found in 37 annotations. Based on these findings, the p-value that seven cell division- or mitosis-related markers are found in the poor prognosis group is estimated to be highly significant (p-value=3.5×10⁻5). In comparison, the absence of cell division- or mitosis-related markers in the good prognosis group is not significant (p-value =0.69). Thus, the seven cell division- or mitosis-related markers may be used as markers for poor prognosis.

Example 8 Construction of an Artificial Reference Pool

[0310] The reference pool for expression profiling in the above Examples was made by using equal amount of cRNAs from each individual patient in the sporadic group. In order to have a reliable, easy-to-made, and large amount of reference pool, a reference pool for breast cancer diagnosis and prognosis can be constructed using synthetic nucleic acid representing, or derived from, each marker gene. Expression of marker genes for individual patient sample is monitored only against the reference pool, not a pool derived from other patients.

[0311] To make the reference pool, 60-mer oligonucleotides are synthesized according to 60-mer ink-jet array probe sequence for each diagnostic/prognostic reporter genes, then double-stranded and cloned into pBluescript SK-vector (Stratagene, La Jolla, Calif.), adjacent to the T7 promoter sequence. Individual clones are isolated, and the sequences of their inserts are verified by DNA sequencing. To generate synthetic RNAs, clones are linearized with EcoRI and a T7 in vitro transcription (IVT) reaction is performed according to the MegaScript kit (Ambion, Austin, Tex.). IVT is followed by DNase treatment of the product. Synthetic RNAs are purified on RNeasy columns (Qiagen, Valencia, Calif.). These synthetic RNAs are transcribed, amplified, labeled, and mixed together to make the reference pool. The abundance of those synthetic RNAs are adjusted to approximate the abundance of the corresponding marker-derived transcripts in the real tumor pool.

Example 9 Use of Single-Channel Data and a Sample Pool Represented by Stored Values

[0312] 1. Creation of a Reference Pool of Stored Values (“Mathematical Sample Pool”)

[0313] The use of ratio-based data used in Examples 1-7, above, requires a physical reference sample. In the above Examples, a pool of sporadic tumor sample was used as the reference. Use of such a reference, while enabling robust prognostic and diagnostic predictions, can be problematic because the pool is typically a limited resource. A classifier method was therefore developed that does not require a physical sample pool, making application of this predictive and diagnostic technique much simpler in clinical applications.

[0314] To test whether single-channel data could be used, the following procedure was developed. First, the single channel intensity data for the 70 optimal genes, described in Example 4, from the 78 sporadic training samples, described in the Materials and Methods, was selected from the sporadic sample vs. tumor pool hybridization data. The 78 samples consisted of 44 samples from patients having a good prognosis and 34 samples from patients having a poor prognosis. Next, the hybridization intensities for these samples were normalized by dividing by the median intensity of all the biological spots on the same microarray. Where multiple microarrays per sample were used, the average was taken across all of the microarrays. A log transform was performed on the intensity data for each of the 70 genes, or for the average intensity for each of the 70 genes where more than one microarray is hybridized, and a mean log intensity for each gene across the 78 sporadic samples was calculated. For each sample, the mean log intensities thus calculated were subtracted from the individual sample log intensity. This figure, the mean subtracted log(intensity) was then treated as the two color log(ratio) for the classifier by substitution into Equation (5). For new samples, the mean log intensity is subtracted in the same manner as noted above, and a mean subtracted log(intensity) calculated.

[0315] The creation of a set of mean log intensities for each gene hybridized creates a “mathematical sample pool” that replaces the quantity-limited “material sample pool.” This mathematical sample pool can then be applied to any sample, including samples in hand and ones to be collected in the future. This “mathematical sample pool” can be updated as more samples become available.

[0316] 2. Results

[0317] To demonstrate that the mathematical sample pool performs a function equivalent to the sample reference pool, the mean-subtracted-log(intensity) (single channel data, relative to the mathematical pool) vs. the log(ratio) (hybridizations, relative to the sample pool) was plotted for the 70 optimal reporter genes across the 78 sporadic samples, as shown in FIG. 22. The ratio and single-channel quantities are highly correlated, indicating both have the capability to report relative changes in gene expression. A classifier was then constructed using the mean-subtracted-log(intensity) following exactly the same procedure as was followed using the ratio data, as in Example 4.

[0318] As shown in FIGS. 23A and 23B, single-channel data was successful at classifying samples based on gene expression patterns. FIG. 23A shows samples grouped according to prognosis using single-channel hybridization data. The white line separates samples from patients classified as having poor prognoses (below) and good prognoses (above). FIG. 23B plots each sample as its expression data correlates with the good (open circles) or poor (filled squares) prognosis classifier parameter. Using the “leave-one-out” cross validation method, the classifier predicted 10 false positives out of 44 samples from patients having a good prognosis, and 6 false negatives out of 34 samples from patients having a poor prognosis, where a poor prognosis is considered a “positive.” This outcome is comparable to the use of the ratio-based classifier, which predicted 7 out of 44, and 6 out of 34, respectively.

[0319] In clinical applications, it is greatly preferable to have few false negatives, which results in fewer under-treated patients. To conform the results to this preference, a classifier was constructed by ranking the patient sample according to its coefficient of correlation to the “good prognosis” template, and choosing a threshold for this correlation coefficient to allow approximately 10% false negatives, i.e., classification of a sample from a patient with poor prognosis as one from a patient with a good prognosis. Out of the 34 poor prognosis samples used herein, this represents a tolerance of 3 out of 34 poor prognosis patients classified incorrectly. This tolerance limit corresponds to a threshold 0.2727 coefficient of correlation to the “good prognosis” template. Results using this threshold are shown in FIGS. 24A and 24B. FIG. 24A shows single-channel hybridization data for samples ranked according to the coefficients of correlation with the good prognosis classifier; samples classified as “good prognosis” lie above the white line, and those classified as “poor prognosis” lie below. FIG. 24B shows a scatterplot of sample correlation coefficients, with three incorrectly classified samples lying to the right of the threshold correlation coefficient value. Using this threshold, the classifier had a false positive rate of 15 out of the 44 good prognosis samples. This result is not very different compared to the error rate of 12 out of 44 for the ratio based classifier.

[0320] In summary, the 70 reporter genes carry robust information about prognosis; the single channel data can predict the tumor outcome almost as well as the ratio based data, while being more convenient in a clinical setting.

Example 10 Comparison of Predictive Power of 70 Optimal Genes to Clinical Predictors and Development of Three Prognosis Categories

[0321] Using inkjet-synthesized oligonucleotide microarrays, we have defined a gene expression profile associated with prognosis in breast cancer. To identify this gene expression profile, tumors of less than 5 cm from lymph node negative patients younger than 55 years were used. Surprisingly, a 70 gene-based classifier outperformed all clinical parameters in predicting distant metastases within 5 years. The odds ratio for metastases of the “poor prognosis” versus “good prognosis” signature group based on the gene expression pattern was estimated to be approximately 15 by a cross-validation procedure. Even though these results were highly encouraging, a limitation of this first study was that these results were derived from and tested on two groups of patients which were selected for outcome: one group of patients who developed distant metastases within 5 years and one group of patients who remained disease-free for at least 5 years.

[0322] To provide a more accurate estimate of the risk of metastases associated with the prognosis signature and to further substantiate that the gene expression profile is a clinically meaningful tool, a cohort of 295 young breast cancer patients including both lymph node negative and positive patients was studied. The findings confirm that the prognosis profile is a more powerful predictor of disease outcome than currently used criteria.

[0323] 1. Breast Tumor Selection Criteria

[0324] A consecutive series of 295 tumors was selected from The Netherlands Cancer Institute (NKI) fresh-frozen tissue bank according to the following patient selection criteria: primary invasive breast carcinoma less than 5 cm at pathologic examination (pTI or pT2); tumor-negative apical axillary lymph node as determined by a negative infraclavicular lymph node biopsy; age at diagnosis 52 years or younger; calendar year of diagnosis 1984-1995; and no prior malignancies. All patients had been treated by modified radical mastectomy or breast conserving surgery, including axillary lymph node dissection, followed by radiotherapy if indicated. The 295 tumor samples included 151 taken from lymph node negative (pathologic examination pN0) patients and 144 lymph node positive (pN+) patients. Ten of the 151 lymph node negative patients and 120 of the 144 lymph node positive patients had received adjuvant systemic therapy consisting of chemotherapy (n=90), hormonal therapy (n=20), or both (n=20). All patients were followed at least annually for a period of at least 5 years. Patient follow-up information was extracted from the NKI Medical Registry. Median follow-up of the 207 patients without metastases as first event was 7.8 years (range: 0.05-18.3) versus 2.7 years (0.3-14.0) for the 88 patients with metastasis as first event during follow-up. For all 295 patients median follow-up is 6.7 years (0.05-18.3). There were no missing data. This study was approved by the Medical Ethical Committee of the Netherlands Cancer Institute.

[0325] Clinicopathological parameters were determined as described in Materials and Methods, above. Estrogen receptor (ER) expression was estimated by hybridization intensity obtained from microarray experiments. Using this assay, it was determined that the cohort of 295 tumor samples includes 69 ER negative (ERα log₁₀ intensity ratio below −0.65 units, corresponding to less than 10% nuclei with positive staining by immunohistochemistry) and 226 ER positive tumors. Histological grade was assessed using the method described by Elston and Ellis, Histopathol. 19(5):403-410 (1991). Vascular invasion was assessed as none (−); minor (1-3 vessels; +/−); major (>3 vessels).

[0326] 2. RNA Isolation and Microarray Expression Profiling

[0327] RNA isolation, cRNA labeling, the 25K oligonucleotide microarrays, and hybridization experiments were as described in Materials and Methods. The statistical error model that assigns p values to expression ratios was as described in Example 4. After hybridization, slides were washed and scanned using a confocal laser scanner (Agilent Technologies) (see Hughes et al., Nat. Biotechnol. 19(4):342-347 (2001)).

[0328] 3. Correlation of the Microarray Data with the Previously Determined Prognosis Profile

[0329] The prognostic value of the gene expression profile in a consecutive series of breast cancer patients was determined using the 70 marker genes identified in the experiments described in Example 4. To acquire this consecutive series, 61 of the pN0 patients that were also part of the training series used for the construction of the 70-gene prognosis profile were also included. Leaving out these patients would have resulted in selection bias, because the first series contained a disproportionally large number of patients who developed distant metastases within 5 years. For each of the 234 new tumors in this 295 tumor sample cohort we calculated the correlation coefficient of the expression of the 70 genes with the previously determined average profile of these genes in tumors of good prognosis patients (C1) (see Example 4). A tumor with a correlation coefficient >0.4 (a threshold previously determined in the training set of 78 tumors that allowed 10% false negatives) was then assigned to the “good prognosis” signature group and all other tumors were assigned to the “poor prognosis” signature group. To avoid overfitting by the 61 previously used pN0 patients, the performance cross-validated correlation coefficients were used for the prognosis classification with a threshold correlation coefficient value of 0.55 (corresponding to the threshold for 10% false negatives of this cross-validated classifier).

[0330] 4. Statistical Analysis

[0331] In the analysis of distant metastasis-free probabilities, patients whose first event was distant metastases were counted as failures; all other patients were censored at the date of their last follow-up, non-breast cancer death, local-regional recurrence or second primary malignancy, including contralateral breast cancer. Time was measured from the date of surgery. Metastasis-free curves were drawn using the method of Kaplan and Meier and compared using the log-rank test. Standard errors (SEs) of the metastasis-free percentages were calculated using the method of Tsiatis (Klein, Scand. J of Statistics 18:333-340 (1991)).

[0332] Proportional hazard regression analysis (Cox, J. R. Statist. Soc. B 34:187-220 (1972)) was used to adjust the association between the correlation coefficient C1 and metastases for other variables. SE's were calculated using the sandwich estimator (Lin and Wei, J. Amer. Stat. Assoc. 84:1074-1079 (1989)). Histological grade, vascular invasion and the number of axillary lymph node metastases (0 vs. 1-3 vs. >4) were used as variables. Linearity of the relation between In (relative hazard) and tumor diameter, age and expression level of ER was tested using the Wald test for non-linear components of restricted cubic splines (Themeau et al., hBiometrika 77:147-160 (1990)). No evidence for non-linearity was found (age: p=0.83, tumor diameter: p=0.75, number of positive nodes: p=0.65 and ER expression level: p=0.27). Non-proportionality of the hazard was tested using the Grambsch and Themeau method (Grambsch and Themeau, Biometrika 81:515-526 (1994)). In addition, for C1 the difference between the ln(hazard ratio) before and after 5 years of follow-up was tested using the Wald test. All calculations were done using the Splus2000 or Splus6 statistical package.

[0333] 5. Prognosis Signature of 295 Breast Cancers

[0334] From each of the 295 tumors, total RNA was isolated and used to generate cRNA, which was labeled and hybridized to microarrays containing 25,000 human genes (see Materials and Methods). Fluorescence intensities of scanned images were quantified and normalized to yield the transcript abundance of a gene as an intensity ratio as compared to a reference pool of cRNA made up of equal amounts of cRNA of all tumors combined. The gene expression ratios of the previously determined 70 prognosis marker genes for all 295 tumors in this study are shown in FIG. 25A. Tumors above (i.e., having a correlation coefficient greater than) the previously determined threshold (dotted line) were assigned to the “good prognosis” category (n=115); those below the line were assigned to the “poor prognosis” category (n=180). FIG. 25B displays the time to distant metastases as a first event (red dots) or the time of follow-up for all other patients (blue dots, see methods). FIG. 25C shows the lymph node status, distant metastases and survival for all 295 patients. By comparing FIGS. 25A, 25B, and 25C, it can be seen that there is a strong correlation between having the good prognosis signature and absence of (early) distant metastases or death. Lymph node negative and positive patients are evenly distributed, indicating that the prognosis profile is independent of lymph node status.

[0335] Table 9 summarizes the association between the prognosis profile and clinical parameters, which reveals that the prognosis profile is associated with histological grade, ER status and age, but not significantly with tumor diameter, vascular invasion, number of positive lymph nodes, or with treatment. TABLE 9 Association of clinical parameters with the prognosis signature groups based on the expression of the preferred 70 prognostic marker genes. Poor signature Good signature (N = 180: 100%) (N = 115: 100%) Variable P-value* Category Number of patients (%) Age 0.0003 <40 52 (29%) 11 (10%) 40-44 41 (23%) 44 (38%) 45-49 55 (31%) 43 (37%) ≧50 32 (18%) 17 (15%) Number pve nodes 0.6 0 (pN0) 91 (51%) 60 (52%) 37258 63 (35%) 43 (37%)  ≧4 26 (14%) 12 (10%) Tumor diameter 0.012 ≦20 mm 84 (47%) 71 (62%) ≧20 mm 96 (53%) 44 (38%) Histologic grade <0.0001 I - Good 19 (11%) 56 (49%) II - Intermediate 56 (31%) 45 (39%) III - Poor 105 (58%)  14 (12%) Vascular invasion 0.38 − 108 (60%)  77 (67%) +/− 18 (10%) 12 (10%) + 54 (30%) 26 (23%) ER expression <0.0001 <−0.65 66 (37%) 3 (3%) ≧−0.65 114 (63%)  112 (97%)  Surgery 0.63 BCT 97 (54%) 64 (56%) Mastectomy 83 (46%) 51 (44%) Chemotherapy 0.79 No 114 (63%)  71 (62%) Yes 66 (37%) 44 (38%) Hormonal therapy 0.63 No 157 (87%)  98 (85%) Yes 23 (13%) 17 (15%)

[0336] 6. Prognostic Value of Gene Expression Signature

[0337] Distant metastasis-free probability and overall survival were calculated for all patients having tumors with either a “good” or “poor prognosis” signature (FIGS. 26A and 26B, Table 10). The resulting Kaplan-Meier curves showed a large difference in metastasis rate and overall survival between the “good prognosis” and “poor prognosis” signature patients. For metastasis as a first event, the hazard ratio (HR) for “poor” versus “good” signature over the whole follow-up period is estimated to be 5.1 (95% CI: 2.9-9.0; p<0.0001). The prognosis profile was even more significant for the first 5 years (HR 8.8; 95% CI: 3.8-20; p<0.0001) as compared to a HR of 1.8 (95% CI: 0.69-4.5; p=0.24) after 5 years. The HR for overall survival is 8.6 (95% CI: 4-19; p<0.0001).

[0338] The prognosis profile was first identified within a selected group of lymph node negative patients. Here, we wished to determine the performance of the prognostic signatures in both lymph node negative and positive patients. In the series of 151 lymph node negative patients (of the 295 patient cohort), the prognosis profile performed extremely well in predicting outcome of disease (FIGS. 26C, 26D; Table 10). For this group of patients, the HR for developing distant metastases is 5.5 (95% CT 2.5-12.2; p<0.0001). To validate our estimated odds ratio for metastases development within five years of our previous study (cross-validated odds ratio 15 (95% CI 4-56; p<0.0001), we calculated the odds ratio for 67 new pN0 patients, who were selected the same way as before (patients with either distant metastases within five years (n=12), or who remained disease-free with a follow-up for at least 5 years (n=55)). The odds ratio of the prognosis classifier for metastases within five years in this validation set is 15.3 (95% CI 1.9-125, p=0.011) (2×2 table, data not shown), in good agreement with our previous findings. These consistent performance results on two sets of tumors, highlight the value of the prognosis profile and the robustness of the profiling technology. Significantly, in the remaining group of 144 lymph node positive patients the prognosis profile was also strongly associated with outcome (FIGS. 26E, 26F, Table 10). Here, the hazard ratio for developing distant metastases is 4.5 (95% CI 2.0-10.2; p=0.0003). TABLE 10 Percentages metastasis-free and overall survival for the prognosis signature groups 5 year 10 year distant distant metastasis metastasis 10 year free^(§) free^(§) 5 year survival survival % (SE) % (SE) % (SE) % (SE) All patients Good signature 94.7% 85.2% 97.4% (1.5%) 94.5% (2.6%) (n = 115) (2.1) (4.3) Poor signature 60.5% 50.6% 74.1% (3.3%) 54.6% (4.4%) (n = 180) (3.8) (4.5) pN0 patients Good signature 93.4% 86.8% 96.7% (2.3%) 96.7% (2.3%) (n = 60) (3.2%) (4.8%) Poor signature 56.2% 44.1% 71.5% (4.8%) 49.6% (6.1%) (n = 91) (5.5%) (6.3%) pN+ patients Good signature 95.2% 82.7% 98.2% (1.8%) 92.0% (4.8%) (n = 55) (2.6) (7.8) Poor signature 66.3% 56.7% 76.5% (4.6%) 59.5% (6.3%) (n = 89) (5.2) (6.4)

[0339] 7. Multivariable Analysis

[0340] Results from the multivariable analysis of distant metastases as first event including age, diameter, number of positive nodes, grade, vascular invasion, ER expression, treatment and the gene expression profile are shown in Table 11. The only independent predictive factors were the 70 gene expression profile, tumor diameter and adjuvant chemotherapy. During the period in which these patients were treated, the majority of premenopausal lymph node positive patients received adjuvant chemotherapy; lymph node negative patients usually did not receive adjuvant treatment. There was improved survival for patients who received adjuvant chemotherapy in this series of tumors. The 70 gene expression profile is by far the strongest predictor for distant metastases with an overall hazard ratio of 4.6 (95% CI: 2.3-9.2; p<0.0001). This is not unexpected, since the prognosis profile was established based on tumors from patients that all developed distant metastases within five years. TABLE 11 Multivariable proportional hazard analysis for metastasis as first event of the prognosis profile in combination with clinicopathological variables. Variable Unit HR 95% CI P-value Profile signature Poor vs. good 4.6  2.3-9.2 <0.0001 Age per 10 years 0.73 0.50-1.06 0.1 No pve nodes per pve node 1.13 1.03-1.24 0.011 Diameter per cm 1.56 1.22-2.0 0.0004 Grade Gr. 2 vs. Gr. 1 1.35 0.61-3.0 0.54 Gr. 3 vs. Gr. 1 1.03 0.44-2.4 Vasc. Invasion ± vs − 0.66 0.30-1.44 0.045 + vs − 1.65 0.98-2.8 ER expression per point 0.86 0.56-1.31 0.48 Surgery Mast. vs. BCT 1.27 0.79-2.0 0.32 Chemotherapy Yes vs. No 0.37 0.20-0.66 0.0008 Hormone Yes vs. No 0.62 0.29-1.34 0.23 Treatment

[0341] The prognosis profile is also a strong predictor of developing distant metastases within the group of lymph node positive patients (see FIGS. 26E, 26F). This is remarkable, since the presence of lymph node metastases by itself is a strong predictor of poor survival. Because most patients with lymph node positive breast cancer in our study received adjuvant chemotherapy or adjuvant hormonal therapy (120 out of 144 patients), it is not possible to give the prognostic value of the profile in untreated lymph node positive patients. There is, however, no indication that there is a difference in the prognostic value of the prognosis profile between patients who received adjuvant chemotherapy compared to those who did not (data not shown).

[0342] A key question is whether the prognosis profile is a more useful clinical tool to determine eligibility for adjuvant systemic treatment than the presently used “St. Gallen” and “NIH-consensus” criteria, which are based on histological and clinical characteristics (see Goldhirsch et al., Meeting Highlights: International Consensus Panel on the Treatment of Primary Breast Cancer, Seventh International Conference on Adjuvant Therapy of Primary Breast Cancer, J. Clin. Oncol. 19(18):3817-3827 (2001); Eifel et al., National Institutes of Health Consensus Development Conference Statement: Adjuvant Therapy for Breast Cancer, Nov. 1-3, 2000, J. Natl. Cancer Inst. 93(13):979-989 (2001)). FIG. 27 shows the Kaplan-Meier metastasis-free curves for the 151 lymph node negative patients, where the patients were classified as “good prognosis/low-risk” or “poor prognosis/high-risk” using the prognosis profile (FIG. 27A), the “St. Gallen” (FIG. 27B) or the “NIH-consensus” criteria (FIG. 27C).

[0343] Two major conclusions can be drawn from this comparison. First, the prognosis profile assigns many more pN0 patients to the low-risk group than the traditional methods (38% for “profile”, versus 15% for “St. Gallen” and 7% for “NIH consensus”). Second, low-risk patients identified by expression profiling have better metastasis-free survival than those classified by “St. Gallen” or “NIH consensus” criteria. Conversely, patients classified as high-risk according to their expression profile tend to develop distant metastases more often than the high risk “St. Gallen” or “NIH consensus” patients. This indicates that both “St. Gallen” and “NIH” criteria misclassify a significant number of patients. Indeed, the high-risk group as defined by “NIH consensus” criteria contains a significant number of patients having a “good prognosis” signature and corresponding outcome (FIG. 27E). Conversely, the low-risk NIH group includes patients with a “poor prognosis” signature and outcome (FIG. 27G). Similar subgroups can be identified within the “St. Gallen” low- and high-risk patients (FIGS. 27D; 27F). Since both “St. Gallen” and “NIH” subgroups contain misclassified patients (who can be better identified through the prognosis signature), these patients are either over- or undertreated in present clinical practice.

[0344] Tumor size is a major parameter used in the “NIH-consensus” criteria for adjuvant therapy selection. However, the data above (see Table 9) show that the ability to develop distant metastases is only partially dependent on tumor size and suggest that metastatic capacity in many tumors is an early and inherent genetic property. The “good prognosis groups” can be subdivided into two groups whose treatment regimens differ. The subgroups were determined by using another threshold in the correlation with the average profile of the good prognosis tumors. In the initial study that identified markers correlated with a good prognosis (see Example 4), we found that tumors having a correlation coefficient of greater than 0.636 (i.e., whose expression profiles correlated most strongly with the average expression profile of the “good prognosis” group) did not give rise to distant metastases. This was determined empirically for the 78 patient tumor samples by determining the correlation coefficient, in the ranked list, above which patients developed no distant metastases (data not shown). Thus, among the tumors previously identified as having a “good prognosis” signature, those that had a correlation coefficient exceeding 0.636 were classified as having a “very good prognosis” signature. These patients with such a “very good prognosis” signature in their tumor (FIGS. 28A-28F, upper line) have an even better outcome of disease than those having an “intermediate prognosis” signature (remaining “good prognosis” signature patients, correlation coefficient between 0.4 and 0.636, FIGS. 28A-28F, middle line). This is true for the entire cohort (FIGS. 28A, 28B) as well as the lymph node negative (FIGS. 28C, 28D) and positive patients separately (FIGS. 28E, 28F).

[0345] Together, our data indicate that the prognosis profile is a more accurate tool to select lymph node negative premenopausal patients for adjuvant systemic therapy than the presently used consensus criteria and may even be useful to guide adjuvant therapy in lymph node positive patients. We propose the following treatment regimens based upon the particular marker expression signature:

[0346] (1) Lymph node negative patients having a tumor with a “very good prognosis” signature can be treated without adjuvant systemic therapy.

[0347] (2) Lymph node negative patients having a tumor with an “intermediate prognosis” signature can be treated with adjuvant hormonal therapy only. As 97% of tumors having the “intermediate prognosis” signature are ER positive, this group of patients should benefit from adjuvant hormonal treatment. Adding chemotherapy to the treatment regimen of this patient group would result in only marginal survival benefit.

[0348] (3) All other patients should receive adjuvant chemotherapy. Where the tumor is ER+, hormonal therapy is also recommended.

[0349] Implementation of the use of the prognostic profile in breast cancer diagnostics should result in improved and patient-tailored adjuvant systemic treatment, reducing both over- and undertreatment.

7. REFERENCES CITED

[0350] All references cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes.

[0351] Many modifications and variations of the present invention can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. The specific embodiments described herein are offered by way of example only, and the invention is to be limited only by the terms of the appended claims along with the full scope of equivalents to which such claims are entitled. 

What is claimed is:
 1. A method for classifying a cell sample as ER(+) or ER(−) comprising detecting a difference in the expression by said cell sample of a first plurality of genes relative to a control, said first plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table
 1. 2. The method of claim 1, wherein said plurality consists of at least 50 of the genes corresponding to the markers listed in Table
 1. 3. The method of claim 1, wherein said plurality consists of at least 100 of the genes corresponding to the markers listed in Table
 1. 4. The method of claim 1, wherein said plurality consists of at least 200 of the genes corresponding to the markers listed in Table
 1. 5. The method of claim 1, wherein said plurality consists of at least 500 of the genes corresponding to the markers listed in Table
 1. 6. The method of claim 1, wherein said plurality consists of at least 1000 of the genes corresponding to the markers listed in Table
 1. 7. The method of claim 1, wherein said plurality consists of each of the genes corresponding to the 2,460 markers listed in Table
 2. 8. The method of claim 1, wherein said plurality consists of the 550 gene markers listed in Table
 2. 9. The method of claim 1, wherein said control comprises nucleic acids derived from a pool of tumors from individual sporadic patients.
 10. The method of claim 1, wherein said detecting comprises the steps of: (a) generating an ER(+) template by hybridization of nucleic acids derived from a plurality of ER(+) patients within a plurality of sporadic patients against nucleic acids derived from a pool of tumors from individual sporadic patients; (b) generating an ER(−) template by hybridization of nucleic acids derived from a plurality of ER(−) patients within said plurality of sporadic patients against nucleic acids derived from said pool of tumors from individual sporadic patients within said plurality; (c) hybridizing an nucleic acids derived from an individual sample against said pool; and (d) determining the similarity of marker gene expression in the individual sample to the ER(+) template and the ER(−) template, wherein if said expression is more similar to the ER(+) template, the sample is classified as ER(+), and if said expression is more similar to the ER(−) template, the sample is classified as ER(−).
 11. A method for classifying a cell sample as BRA4CA1-related or sporadic, comprising detecting a difference in the expression of a first plurality of genes relative to a control, said first plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table
 3. 12. The method of claim 11, wherein said plurality consists of at least 50 of the genes corresponding to the markers listed in Table
 3. 13. The method of claim 11, wherein said plurality consists of at least 100 of the genes corresponding to the markers listed in Table
 3. 14. The method of claim 11, wherein said plurality consists of at least 200 of the genes corresponding to the markers listed in Table
 3. 15. The method of claim 11, wherein said plurality consists of each of the genes corresponding to the 430 markers listed in Table
 3. 16. The method of claim 11, wherein said plurality consists of each of the genes corresponding to the 100 markers listed in Table
 4. 17. The method of claim 11, wherein said control comprises nucleic acids derived from a pool of tumors from individual sporadic patients.
 18. The method of claim 11, wherein said detecting comprises the steps of (a) generating a BRCA1 template by hybridization of nucleic acids derived from a plurality of BRCA1 patients within a plurality of ER(−) patients against nucleic acids derived from a pool of tumors; (b) generating a sporadic template by hybridization of nucleic acids derived from a plurality of sporadic patients within said plurality of ER(−) patients against nucleic acids derived from said pool of tumors; (c) hybridizing nucleic acids derived from an individual sample against said pool; and (d) determining the similarity of marker gene expression in the individual sample to the BRCA1 template and the sporadic template, wherein if said expression is more similar to the BRCA1 template, the sample is classified as BRCA1, and if said expression is more similar to the sporadic template, the sample is classified as sporadic.
 19. A method for classifying an individual as having a good prognosis (no distant metastases within five years of initial diagnosis) or a poor prognosis (distant metastases within five years of initial diagnosis), comprising detecting a difference in the expression of a first plurality of genes in a cell sample taken from the individual relative to a control, said first plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table
 5. 20. The method of claim 19, wherein said plurality consists of at least 20 of the genes corresponding to the markers listed in Table
 5. 21. The method of claim 19, wherein said plurality consists of at least 100 of the genes corresponding to the markers listed in Table
 5. 22. The method of claim 19, wherein said plurality consists of at least 150 of the genes corresponding to the markers listed in Table
 5. 23. The method of claim 19, wherein said plurality consists of each of the genes corresponding to the 231 markers listed in Table
 5. 24. The method of claim 19, wherein said plurality consists of the 70 gene markers listed in Table
 6. 25. The method of claim 1, wherein said control comprises nucleic acids derived from a pool of tumors from individual sporadic patients.
 26. The method of claim 19, wherein said detecting comprises the steps of: (a) generating a good prognosis template by hybridization of nucleic acids derived from a plurality of good prognosis patients against nucleic acids derived from a pool of tumors from individual patients; (b) generating a poor prognosis template by hybridization of nucleic acids derived from a plurality of poor prognosis patients against nucleic acids derived from said pool of tumors from said plurality of individual patients; (c) hybridizing an nucleic acids derived from and individual sample against said pool; and (d) determining the similarity of marker gene expression in the individual sample to the good prognosis template and the poor prognosis template, wherein if said expression is more similar to the good prognosis template, the sample is classified as having a good prognosis, and if said expression is more similar to the poor prognosis template, the sample is classified as having a poor prognosis.
 27. The method of claim 1, wherein the cell sample is additionally classified as BRCA1-related or sporadic by detecting a difference in the expression of a second plurality of genes in a cell sample taken from the individual relative to a control, said second plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table 3 or Table
 4. 28. The method of claim 1, wherein the cell sample is additionally classified as taken from a patient with a good prognosis or a poor prognosis by detecting a difference in the expression of a second plurality of genes in a cell sample taken from the individual relative to a control, said second plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table
 5. 29. The method of claim 11, wherein the cell sample is additionally classified as taken from a patient with a good prognosis or a poor prognosis by detecting a difference in the expression of a second plurality of genes in a cell sample taken from the individual relative to a control, said second plurality of genes consisting of at least 20 of the genes corresponding to the markers listed in Table
 5. 30. The method of claim 11, wherein the cell sample is additionally classified as ER(+) or ER(−) by detecting a difference in the expression of a second plurality of genes in a cell sample taken from the individual relative to a control, said second plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table
 1. 31. The method of claim 19, wherein the cell sample is additionally classified as ER(+) or ER(−) by detecting a difference in the expression of a second plurality of genes in a cell sample taken from the individual relative to a control, said second plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table
 1. 32. The method of claim 19, wherein the cell sample is additionally classified as BRCA1 or sporadic by detecting a difference in the expression of a second plurality of genes in a cell sample taken from the individual relative to a control, said second plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table
 3. 33. A method for classifying a sample as ER(+) or ER(−) by calculating the similarity between the expression of at least 5 of the markers listed in Table 1 in the sample to the expression of the same markers in an ER(−) nucleic acid pool and an ER(+) nucleic acid pool, comprising the steps of: (a) labeling nucleic acids derived from a sample, with a first fluorophore to obtain a first pool of fluorophore-labeled nucleic acids; (b) labeling with a second fluorophore a first pool of nucleic acids derived from two or more ER(+) samples, and a second pool of nucleic acids derived from two or more ER(−) samples: (c) contacting said first fluorophore-labeled nucleic acid and said first pool of second fluorophore-labeled nucleic acid with a first microarray under conditions such that hybridization can occur, and contacting said first fluorophore-labeled nucleic acid and said second pool of second fluorophore-labeled nucleic acid with a second microarray under conditions such that hybridization can occur, wherein said first microarray and said second microarray are similar to each other, exact replicas of each other, or are identical, detecting at each of a plurality of discrete loci on the first microarray a first flourescent emission signal from said first fluorophore-labeled nucleic acid and a second fluorescent emission signal from said first pool of second fluorophore-labeled genetic matter that is bound to said first microarray under said conditions, and detecting at each of the marker loci on said second microarray said first fluorescent emission signal from said first fluorophore-labeled nucleic acid and a third fluorescent emission signal from said second pool of second fluorophore-labeled nucleic acid; (d) determining the similarity of the sample to the ER(−) and ER(+) pools by comparing said first fluorescence emission signals and said second fluorescence emission signals, and said first emission signals and said third fluorescence emission signals; and (e) classifying the sample as ER(+) where the first fluorescence emission signals are more similar to said second fluorescence emission signals than to said third fluorescent emission signals, and classifying the sample as ER(−) where the first fluorescence emission signals are more similar to said third fluorescence emission signals than to said second fluorescent emission signals.
 34. The method of claim 33, wherein said similarity is calculated by determining a first sum of the differences of expression levels for each marker between said first fluorophore-labeled nucleic acid and said first pool of second fluorophore-labeled nucleic acid, and a second sum of the differences of expression levels for each marker between said first fluorophore-labeled nucleic acid and said second pool of second fluorophore-labeled nucleic acid, wherein if said first sum is greater than said second sum, the sample is classified as ER(−), and if said second sum is greater than said first sum, the sample is classified as ER(+).
 35. The method of claim 33, wherein said similarity is calculated by computing a first classifier parameter P₁ between an ER(+) template and the expression of said markers in said sample, and a second classifier parameter P₂ between an ER(−) template and the expression of said markers in said sample, wherein said P₁ and P₂ are calculated according to the formula: P _(i)=({right arrow over (z)} _(i) •{right arrow over (y)})/(∥{right arrow over (z)} _(i) ∥·∥{right arrow over (y)}∥), wherein {right arrow over (z)}₁ and {right arrow over (z)}₂ are ER(+) and ER(−) templates, respectively, and are calculated by averaging said second fluorescence emission signal for each of said markers in said first pool of second fluorophore-labeled nucleic acid and said third fluorescence emission signal for each of said markers in said second pool of second fluorophore-labeled nucleic acid, respectively, and wherein {right arrow over (y)} is said first fluorescence emission signal of each of said markers in the sample to be classified as ER(+) or ER(−), wherein the expression of the markers in the sample is similar to ER(−) if P₁<P₂, and similar to ER(+) if P₁>P₂.
 36. A method for determining a set of marker genes whose expression is associated with a particular phenotype, comprising the steps of: (a) selecting phenotype having two or more phenotype categories; (b) identifying a plurality of genes wherein the expression of said genes is correlated or anticorrelated with one of the phenotype categories, and wherein the correlation coefficient for each gene is calculated according to the equation ρ=({right arrow over (c)}•{right arrow over (r)})/(∥{right arrow over (c)}∥·{right arrow over (r)}∥), wherein C is a number representing said phenotype category and {right arrow over (r)} is the logarithmic expression ratio across all the samples for each individual gene, wherein if the correlation coefficient has an absolute value of 0.3 or greater, said expression of said gene is associated with the phenotype category, wherein said plurality of genes is a set of marker genes whose expression is associated with a particular phenotype.
 37. The method of claim 36, wherein said set of marker genes is validated by: (a) using a statistical method to randomize the association between said marker genes and said phenotype category, thereby creating a control correlation coefficient for each marker gene; (b) repeating step (a) one hundred or more times to develop a frequency distribution of said control correlation coefficients for each marker gene; (c) determining the number of marker genes having a control correlation coefficient of 0.3 or above, thereby creating a control marker gene set; and (d) comparing the number of control marker genes so identified to the number of marker genes, wherein if the p value of the difference between the number of marker genes and the number of control genes is less than a threshold, said set of marker genes is validated.
 38. The method of claim 36, wherein said set of marker genes is optimized by the method comprising: (a) rank-ordering the genes by amplitude of correlation or by significance of the correlation coefficients to create a rank-ordered list, and (b) selecting an arbitrary number n of marker genes from the top of the rank-ordered list.
 39. The method of claim 38, wherein said set of marker genes is further optimized by the method comprising: (a) calculating an error rate for said arbitrary number n of marker genes; (b) increasing by 1 the number of genes selected from the top of the rank-ordered list; (c) calculating an error rate for said number of genes selected from the top of the rank-ordered list; (d) repeating steps (b) and (c) until said number of genes selected from the top of the rank-ordered list includes all genes included in said rank ordered list, and (e) identifying said number of genes selected from the top of the rank-ordered list for which the error rate is smallest, wherein said set of marker genes is optimized when the error rate is the smallest.
 40. A method for assigning a person to one of a plurality of categories in a clinical trial, comprising determining for each said person the level of expression of at least five of the prognosis markers listed in Table 6, determining therefrom whether the person has an expression pattern that correlates with a good prognosis or a poor prognosis, and assigning said person to one category in a clinical trial if said person is determined to have a good prognosis, and a different category if that person is determined to have a poor prognosis.
 41. A method of classifying a first cell or organism as having one of at least two different phenotypes, said at least two different phenotypes comprising a first phenotype and a second phenotype, said method comprising: (a) comparing the level of expression of each of a plurality of genes in a first sample from the first cell or organism to the level of expression of each of said genes, respectively, in a pooled sample from a plurality of cells or organisms, said plurality of cells or organisms comprising different cells or organisms exhibiting said at least two different phenotypes, respectively, to produce a first compared value; (b) comparing said first compared value to a second compared value, wherein said second compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having said first phenotype to the level of expression of each of said genes, respectively, in said pooled sample; (c) comparing said first compared value to a third compared value, wherein said third compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having said second phenotype to the level of expression of each of said genes, respectively, in said pooled sample, (d) optionally carrying out one or more times a step of comparing said first compared value to one or more additional compared values, respectively, each additional compared value being the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having a phenotype different from said first and second phenotypes but included among said at least two different phenotypes, to the level of expression of each of said genes, respectively, in said pooled sample; and (e) determining to which of said second, third and, if present, one or more additional compared values, said first compared value is most similar; wherein said first cell or organism is determined to have the phenotype of the cell or organism used to produce said compared value most similar to said first compared value.
 42. The method of claim 40, wherein said compared values are each ratios of the levels of expression of each of said genes.
 43. The method of claim 40, wherein each of said levels of expression of each of said genes in said pooled sample are normalized prior to any of said comparing steps.
 44. The method of claim 42 wherein normalizing said levels of expression is carried out by dividing each of said levels of expression by the median or mean level of expression of each of said genes or dividing by the mean or median level of expression of one or more housekeeping genes in said pooled sample.
 45. The method of claim 42 wherein said normalized levels of expression are subjected to a log transform and said comparing steps comprise subtracting said log transform from the log of said levels of expression of each of said genes in said sample from said cell or organism.
 46. The method of claim 40, wherein said at least two different phenotypes are different stages of a disease or disorder.
 47. The method of claim 40, wherein said at least two different phenotypes are different prognoses of a disease or disorder.
 48. The method of claim 40, wherein said levels of expression of each of said genes, respectively, in said pooled sample or said levels of expression of each of said genes in a sample from said cell or organism characterized as having said first phenotype, said second phenotype, or said phenotype different from said first and second phenotypes, respectively, are stored on a computer.
 49. A microarray comprising at least 5 markers derived from any one of Tables 1-6, wherein at least 50% of the probes on the microarray are present in any one of Tables 1-6.
 50. The microarray of claim 48, wherein at least 70% of the probes on the microarray are present in any one of Tables 1-6.
 51. The microarray of claim 48, wherein at least 80% of the probes on the microarray are present in any one of Tables 1-6.
 52. The microarray of claim 48, wherein at least 90% of the probes on the microarray are present in any one of Tables 1-6.
 53. The microarray of claim 48, wherein at least 95% of the probes on the microarray are present in any one of Tables 1-6.
 54. The microarray of claim 48, wherein at least 98% of the probes on the microarray are present in any one of Tables 1-6.
 55. A microarray for distinguishing ER(+) and ER(−) cell samples comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a different gene, said plurality consisting of at least 20 of the genes corresponding to the markers listed in Table 1 or Table 2, wherein at least 50% of the probes on the microarray are present in Table 1 or Table
 2. 56. A microarray for distinguishing BRCA1-related and sporadic cell samples comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a different gene, said plurality consisting of at least 20 of the genes corresponding to the markers listed in Table 3 or Table 4, wherein at least 50% of the probes on the microarray are present in Table 3 or Table
 4. 57. A microarray for distinguishing cell samples from individuals having a good prognosis and cell samples from individuals having a poor prognosis, comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a different, said plurality consisting of at least 20 of the genes corresponding to the markers listed in Table 5 or Table 6, wherein at least 50% of the probes on the microarray are present in Table 5 or Table
 6. 58. A kit for determining whether a sample contains a BRCA1 or sporadic mutation, comprising at least one microarray comprising probes to at least 20 of the genes corresponding to the markers listed in Table 3, and a computer readable medium having recorded thereon one or more programs for determining the similarity of the level of nucleic acid derived from the markers listed in Table 3 in a sample to that in a BRCA1 pool and a sporadic tumor pool, wherein the one or more programs cause a computer to perform a method comprising computing the aggregate differences in expression of each marker between the sample and BRCA1 and the aggregate differences in expression of each marker between the sample and sporadic pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the BRCA1 and sporadic pools, said correlation calculated according to Equation (3).
 59. A kit for determining the ER-status of a sample, comprising at least one microarray comprising probes to at least 20 of the genes corresponding to the markers listed in Table 1, and a computer readable medium having recorded thereon one or more programs for determining the similarity of the level of nucleic acid derived from the markers listed in Table 1 in a sample to that in an ER(−) pool and an ER(+) pool, wherein the one or more programs cause a computer to perform a method comprising computing the aggregate differences in expression of each marker between the sample and ER(−) pool and the aggregate differences in expression of each marker between the sample and ER(+) pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the ER(−) and ER(+) pools, said correlation calculated according to Equation (3).
 60. A kit for determining whether a sample is derived from a patient having a good prognosis or a poor prognosis, comprising at least one microarray comprising probes to at least 20 of the genes corresponding to the markers listed in Table 5, and a computer readable medium having recorded thereon one or more programs for determining the similarity of the level of nucleic acid derived from the markers listed in Table 5 in a sample to that in a pool of samples derived from individuals having a good prognosis and a pool of samples derived from individuals having a good prognosis, wherein the one or more programs cause a computer to perform a method comprising computing the aggregate differences in expression of each marker between the sample and the good prognosis pool and the aggregate differences in expression of each marker between the sample and the poor prognosis pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the good prognosis and poor prognosis pools, said correlation calculated according to Equation (3).
 61. A method for classifying a breast cancer patient according to prognosis, comprising: (a) comparing the respective levels of expression of at least five genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient to respective control levels of expression of said at least five genes; and (b) classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said levels of expression in said cell sample and said control levels.
 62. The method according to claim 61, wherein step (b) comprises determining whether said similarity exceeds one or more predetermined threshold values of similarity.
 63. A method for classifying a breast cancer patient according to prognosis, comprising: (a) determining the similarity between the level of expression of each of at least five genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient, to control levels of expression for each respective said at least five genes to obtain a patient similarity value; (b) providing selected first and second threshold values of similarity of said level of expression of each of said at least five genes to said control levels of expression to obtain first and second similarity threshold values, respectively, wherein said second similarity threshold indicates greater similarity to said control than does said first similarity threshold; and (c) classifying said breast cancer patient as having a first prognosis if said patient similarity value exceeds said first and said second similarity threshold values, a second prognosis if said level of expression of said genes exceeds said first similarity threshold value but does not exceed said second similarity threshold value, and a third prognosis if said level of expression of said genes does not exceed said first similarity threshold value or said second similarity threshold value.
 64. The method of claim 63, further comprising determining prior to step (a) said level of expression of said at least five genes.
 65. The method of claim 61, wherein said control levels are the mean levels of expression of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have no distant metastases within five years of initial diagnosis.
 66. The method of claim 61, wherein said control levels comprise the expression levels of said genes in breast cancer patients who have had no distant metastases within five years of initial diagnosis.
 67. The method of claim 61, wherein said control levels comprise, for each of said at least five genes, mean log intensity values stored on a computer.
 68. The method of claim 61, wherein said control levels comprise, for each of said at least five genes, the mean log intensity values that are listed in Table
 7. 69. The method of claim 63, wherein said determining in step (a) is carried out by a method comprising determining the degree of similarity between the level of expression of each of said at least five genes in a sample taken from said breast cancer patient to the level of expression of each of said at least five genes in a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis.
 70. The method of claim 63, wherein said determining in step (a) is carried out by a method comprising determining the difference between the absolute expression level of each of said at least five genes and the average expression level of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis.
 71. The method of claim 63, wherein said first threshold value and said second threshold value are coefficients of correlation to the mean expression level of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis.
 72. The method of claim 71, wherein said first threshold similarity value and said second threshold similarity values are selected by a method comprising: (a) rank ordering in descending order said tumor samples that compose said pool of tumor samples by the degree of similarity between the level of expression of each said at least five genes in each of said tumor samples to the mean level of expression of said at least five genes of the remaining tumor samples that compose said pool to obtain a rank-ordered list, said degree of similarity being expressed as a similarity value; (b) determining an acceptable number of false negatives in said classifying step, wherein a false negative is a breast cancer patient for whom the expression levels of said at least five genes in said cell sample predicts that said breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis; (c) determining a similarity value above which in said rank ordered list fewer than said acceptable number of tumor samples are false negatives; (d) selecting said similarity value determined in step (c) as said first threshold similarity value; and (e) selecting a second similarity value, greater than said first similarity value, as said second threshold similarity value.
 73. The method of claim 72, wherein said second threshold similarity value is selected in step (e) by a method comprising determining which of said tumor samples, taken from said breast cancer patients having a distant metastasis within the first five years after initial diagnosis, in said rank ordered list has the greatest similarity value, and selecting said greatest similarity value as said second threshold similarity value.
 74. The method of claim 72, wherein said first and second threshold similarity values are correlation coefficients, and said first threshold similarity value is 0.4 and said second threshold similarity value is greater than 0.4.
 75. The method of claim 72, wherein said first and second threshold similarity values are correlation coefficients, and said second threshold similarity value is 0.636.
 76. The method of claim 61, wherein said comparing step (a) comprises comparing the respective levels of expression of at least ten of said genes for which markers are listed in Table 5 in said cell sample to said respective control levels of said at least ten of said genes, wherein said control levels of expression of said at least ten genes are the average expression levels of each of said at least ten genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis.
 77. The method of claim 61, wherein said comparing step (a) comprises comparing the respective levels of expression of at least 25 of said genes for which markers are listed in Table 5 in said cell sample to said respective control levels of expression of said at least 25 genes, wherein said control levels of expression of said at least 25 genes are the average expression levels of each of said at least 25 genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis.
 78. The method of claim 61, wherein said comparing step (a) comprises comparing the respective levels of expression of each of said genes for which markers are listed in Table 6 in said cell sample to said respective control levels of expression of each of said genes for which markers are listed in Table 6, wherein said control levels of expression of each of said genes for which markers are listed in Table 6 are the average expression levels of each of said genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis.
 79. A method of assigning a therapeutic regimen to a breast cancer patient, comprising: (a) classifying said patient as having a “poor prognosis,” “intermediate prognosis,” or “very good prognosis” on the basis of the levels of expression of at least five genes for which markers are listed in Table 5; and (b) assigning said patient a therapeutic regimen, said therapeutic regimen (i) comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or (ii) comprising chemotherapy if said patient has any other combination of lymph node status and expression profile.
 80. A method of assigning a therapeutic regimen to a breast cancer patient, comprising: (a) determining the lymph node status for said patient; (b) determining the level of expression of at least five genes for which markers are listed in Table 5 in a cell sample from said patient, thereby generating an expression profile; (c) classifying said patient as having a “poor prognosis,” “intermediate prognosis,” or “very good prognosis” on the basis of said expression profile; and (d) assigning said patient a therapeutic regimen, said therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and classification.
 81. The method of claim 80 in which said therapeutic regimen assigned to lymph node negative patients classified as having an “intermediate prognosis” additionally comprises adjuvant hormonal therapy.
 82. The method of claim 80, wherein said classifying step (c) is carried out by a method comprising: (a) rank ordering in descending order a plurality of breast cancer tumor samples that compose a pool of breast cancer tumor samples by the degree of similarity between the level of expression of said at least five genes in each of said tumor samples and the level of expression of said at least five genes across all remaining tumor samples that compose said pool, said degree of similarity being expressed as a similarity value; (b) determining an acceptable number of false negatives in said classifying step, wherein a false negative is a breast cancer patient for whom the expression levels of said at least five genes in said cell sample predicts that said breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis; (c) determining a similarity value above which in said rank ordered list said acceptable number of tumor samples or fewer are false negatives; (d) selecting said similarity value determined in step (c) as a first threshold similarity value; (e) selecting a second similarity value, greater than said first similarity value, as a second threshold similarity value; and (f) determining the similarity between the level of expression of each of said at least five genes in a breast cancer tumor sample from the breast cancer patient and the level of expression of each of said respective at least five genes in said pool, to obtain a patient similarity value, wherein if said patient similarity value equals or exceeds said second threshold similarity value, said patient is classified as having a “very good prognosis”; if said patient similarity value equals or exceeds said first threshold similarity value, but is less than said second threshold similarity value, said patient is classified as having an “intermediate prognosis”; and if said patient similarity value is less than said first threshold similarity value, said patient is classified as having a “poor prognosis.”
 83. The method of claim 80 which further comprises determining the estrogen receptor (ER) status of said patient, wherein if said patient is ER positive and lymph node negative, said therapeutic regimen assigned to said patient additionally comprises adjuvant hormonal therapy.
 84. The method of claim 80, wherein said patient is 52 years of age or younger.
 85. The method of claim 80 or 84, wherein said patient has stage I or stage II breast cancer.
 86. The method of claim 80, wherein said patient is premenopausal.
 87. A method of classifying a breast cancer patient according to prognosis comprising the steps of: (a) contacting first nucleic acids derived from a tumor sample taken from said breast cancer patient, and second nucleic acids derived from two or more tumor samples from breast cancer patients who have had no distant metastases within five years of initial diagnosis, with an array under conditions such that hybridization can occur, said array comprising a positionally-addressable ordered array of polynucleotide probes bound to a solid support, said polynucleotide probes being complementary and hybridizable to at least five of the genes respectively for which markers are listed in Table 5, or the RNA encoded by said genes, and wherein at least 50% of the probes on said array are hybridizable to genes respectively for which markers are listed in Table 5, or to the RNA encoded by said genes; (b) detecting at each of a plurality of discrete loci on said array a first fluorescent emission signal from said first nucleic acids and a second fluorescent emission signal from said second nucleic acids that are bound to said array under said conditions; (c) calculating the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes respectively for which markers are listed in Table 5; and (d) classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes respectively for which markers are listed in Table
 5. 88. A computer program product for classifying a breast cancer patient according to prognosis, the computer program product for use in conjunction with a computer having a memory and a processor, the computer program product comprising a computer readable storage medium having a computer program encoded thereon, wherein said computer program product can be loaded into the one or more memory units of a computer and causes the one or more processor units of the computer to execute the steps of: (a) receiving a first data structure comprising the respective levels of expression of each of at least five genes for which markers are listed in Table 5 in a cell sample taken from said patient; (b) determining the similarity of the level of expression of each of said at least five genes to respective control levels of expression of said at least five genes to obtain a patient similarity value; (c) comparing said patient similarity value to selected first and second threshold values of similarity of said respective levels of expression of each of said at least five genes to said respective control levels of expression of said at least five genes, wherein said second threshold value of similarity indicates greater similarity to said respective control levels of expression of said at least five genes than does said first threshold value of similarity; and (d) classifying said patient as having a first prognosis if said patient similarity value exceeds said first and said second threshold similarity values; a second prognosis if said patient similarity value exceeds said first threshold similarity value but does not exceed said second threshold similarity value; and a third prognosis if said patient similarity value does not exceed said first threshold similarity value or said second threshold similarity value.
 89. The computer program product of claim 88, wherein said first threshold value of similarity and said second threshold value of similarity are values stored in said computer.
 90. The computer program product of claim 88, wherein said respective control levels of expression of said at least five genes is stored in said computer.
 91. The computer program product of claim 88 wherein said first prognosis is a “very good prognosis”; said second prognosis is an “intermediate prognosis”; and said third prognosis is a “poor prognosis”; wherein said computer program may be loaded into the memory and further cause said one or more processor units of said computer to execute the step of assigning said breast cancer patient a therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and expression profile.
 92. The computer program product of claim 91 wherein said clinical data includes the lymph node and estrogen receptor (ER) status of said breast cancer patient.
 93. The computer program product of claim 88 wherein said computer program may be loaded into the memory and further causes said one or more processor units of the computer to execute the steps of receiving a data structure comprising clinical data specific to said breast cancer patient.
 94. The computer program product of claim 88 wherein said respective control levels of expression of said at least five genes comprises a set of single-channel mean hybridization intensity values for each of said at least five genes, stored on said computer readable storage medium.
 95. The computer program product of claim 93 wherein said single-channel mean hybridization intensity values are log transformed.
 96. The computer program product of claim 88 wherein said computer program product causes said processing unit to perform said comparing step (c) by calculating the difference between the level of expression of each of said atleast five genes in said cell sample taken from said breast cancer patient and said respective control levels of expression of said at least five genes.
 97. The computer program product of claim 88 wherein said computer program product causes said processing unit to perform said comparing step (c) by calculating the mean log level of expression of each of said at least five genes in said control to obtain a control mean log expression level for each gene, calculating the log expression level for each of said at least five genes in a breast cancer sample from said patient to obtain a patient log expression level, and calculating the difference between the patient log expression level and the control mean log expression for each of said at least five genes.
 98. The computer program product of claim 88 wherein said computer program product causes said processing unit to perform said comparing step (c) by calculating similarity between the level of expression of each of said at least five genes in said cell sample taken from said patient and said respective control levels of expression of said at least five genes, wherein said similarity is expressed as a similarity value.
 99. The computer program product of claim 98 wherein said similarity value is a correlation coefficient. 